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Bioelectrical and Biophysical Interactions Across Spatio-Temporal Domains: Identifying the
Conduit for the Emergence of Material from the Immaterial
by
Trevor N. Carniello
A Manuscript submitted in partial fulfillment of the requirements for the degree of
Master of Science (M.Sc) in Biology
The Faculty of Graduate Studies
Laurentian University
Sudbury, Ontario, Canada
© Trevor N. Carniello, 2015
ii
THESIS DEFENCE COMMITTEE/COMITÉ DE SOUTENANCE DE THÈSE
Laurentian Université/Université Laurentienne
Faculty of Graduate Studies/Faculté des études supérieures
Title of Thesis
Titre de la thèse Bioelectrical and Biophysical Interactions Across Spatio-Temporal Domains:
Identifying the Conduit for the Emergence of Material from the Immaterial
Name of Candidate
Nom du candidat Carniello, Trevor
Degree
Diplôme Master of Science
Department/Program Date of Defence
Département/Programme Biology Date de la soutenance January 15, 2016
APPROVED/APPROUVÉ
Thesis Examiners/Examinateurs de thèse:
Dr. Michael Persinger
(Supervisor/Directeur(trice) de thèse)
Dr. Rob Lafrenie
(Committee member/Membre du comité)
Dr. Peter Ryser
(Committee member/Membre du comité)
Approved for the Faculty of Graduate Studies
Approuvé pour la Faculté des études supérieures
Dr. David Lesbarrères
Monsieur David Lesbarrères
Dr. Michel Cifra Acting Dean, Faculty of Graduate Studies
(External Examiner/Examinateur externe) Doyen intérimaire, Faculté des études supérieures
ACCESSIBILITY CLAUSE AND PERMISSION TO USE
I, Trevor Carniello, hereby grant to Laurentian University and/or its agents the non-exclusive license to archive and
make accessible my thesis, dissertation, or project report in whole or in part in all forms of media, now or for the
duration of my copyright ownership. I retain all other ownership rights to the copyright of the thesis, dissertation or
project report. I also reserve the right to use in future works (such as articles or books) all or part of this thesis,
dissertation, or project report. I further agree that permission for copying of this thesis in any manner, in whole or in
part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their
absence, by the Head of the Department in which my thesis work was done. It is understood that any copying or
publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written
permission. It is also understood that this copy is being made available in this form by the authority of the copyright
owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted
by the copyright laws without written authority from the copyright owner.
iii
Abstract
The emergence of biophysical and bioelectrical dynamics at the level of the organism are
explored in an attempt to identify the physiological correlates of the onset of senescence in
plants. Furthermore, the ability of phylogenically disparate entities to demonstrate the potential
to simultaneously display correlated electro-dynamic processes akin to sharing the "same space"
or spatial parameters is investigated. The underlying, aforementioned electrophysiological
changes associated with senescence and the potential for phylogenically disparate systems to
display markedly correlated processes may share water as a common source of variance. The
imperative investigations on the physical nature of water and its interactions with applied
electromagnetic fields with respect to enhanced latency of the onset of deviations in pH and
enhanced electro-dynamic correlations are experimentally explored. A hypothesis, supplemented
by marked quantitative convergence, suggests that water is the central conduit necessary for the
emergence of material from the immaterial.
Keywords
Excess correlation, immortality, material, energy, electrodynamics, electric fields,
magnetic fields, bacteria, plants, electrical potential.
iv
Acknowledgements
I wish to acknowledge, in this section, four individuals whose influence were instrumental for
the development of this particular dissertation. First and foremost, I wish to thank, although his
influence could not be directly appreciated, Michael Faraday. His ingenuity and practical
exploratory nature, along with his remarkable ability to experimentally demonstrate the
geometric nature of electromagnetic phenomenon has greatly inspired this author. To a most
imaginative and brilliant individual, much gratitude. Secondly, I wish to acknowledge Hermann
Minkowski for his contribution to the physical understanding of space and time. For your
geometrical interpretation of 4 dimensional coordinates systems, the basis of which time is
introduced as the 4th parameter, has provided the necessary contingencies for the quantification
of most of this document. My penultimate acknowledgement is directed towards a much under-
appreciated scientist, Sir Arthur Stanley Eddington. In memoriam of this great individual I have
dedicated the contents of the 6th chapter. For a man who verified Einstein's hypothesis that large
masses distort the physical construct of space-time, whose mathematical interpretation of the
limits of the Universal set, as described as Eddignton's number whose value approaches the
approximate number of atoms in the Universe, and whose insight to the nature of the physical is
unparalleled, my deepest admiration. Finally I wish to thank, and cannot thank enough, my
mentor Dr. Michael A. Persinger. For your most patience, insight, guidance and appropriate
differential reinforcement of my abilities, I am indebted to you. My perpetual continuation and
fruitful futuristic contributions to all matters, personal and scientific, I attribute to the skills that I
have acquired under your mentorship. I am looking forward to my continued work under your
supervision, acquiring and refining new found skill that will propel me to achieve my potential.
v
I would also like to acknowledge my committee members Dr. Peter Ryser and Dr. Robert
Lafrenie for your assistance and participation in this journey.
vi
Table of Contents
ABSTRACT .............................................................................................................................................. III
KEYWORDS ............................................................................................................................................ III
ACKNOWLEDGEMENTS ....................................................................................................................... IV
LIST OF TABLES ...................................................................................................................................... X
LIST OF FIGURES ................................................................................................................................. XII
CHAPTER 1 ............................................................................................................................................... 1
1 INTRODUCTION TO THE BLUEPRINT FOR IMMORTALITY ................................................. 1
1.1 References .............................................................................................................................................. 14
CHAPTER 2 ............................................................................................................................................. 18
2 ELECTROPHYSIOLOGY OF THE DYNAMIC PROFILES OF PLANTS UNDERGOING
SENESCENCE .......................................................................................................................................... 18
2.1 Abstract : ................................................................................................................................................. 18
2.2 Introduction: ........................................................................................................................................... 18
2.2.1 Physiological Perspectives of Senescence .................................................................................................. 18
2.2.2 Electrophysiological Perspectives: The possibility of using light-mediated alterations to affect
electrophysiological responses in plants in order to assess differential stages of senescence .............................. 20
2.3 Methods: ................................................................................................................................................. 23
2.3.1 Measurement devices ................................................................................................................................ 23
2.3.2 Specimen .................................................................................................................................................... 23
2.3.3 Sensor placement ....................................................................................................................................... 24
2.3.4 Leaf selection ............................................................................................................................................. 24
2.3.5 Electrophysiological manipulation ............................................................................................................. 25
2.4 Results: ................................................................................................................................................... 26
2.4.1 The effects of intensity and photoperiodicity ............................................................................................ 26
vii
2.4.2 Weekly variations in electrodynamic responses to peak intensities: ........................................................ 28
2.4.3 Common sources of variance: .................................................................................................................... 30
2.4.4 Classification of distinct stages of senescence ........................................................................................... 31
2.4.5 Temporal Dynamics .................................................................................................................................... 33
2.5 Discussion: .............................................................................................................................................. 34
2.5.1 Electrophysiological Correlates of Senescence .......................................................................................... 34
2.5.2 Temporal Variation of Senescence Onset .................................................................................................. 37
2.5.3 Quantitative Support for Electrophysiological Alterations ........................................................................ 37
2.6 References : ............................................................................................................................................ 44
CHAPTER 3 ............................................................................................................................................. 47
3 EXPERIMENTAL TRANS-SPECIES EXCESS CORRELATION ............................................. 47
3.1 Abstract .................................................................................................................................................. 47
3.2 Introduction ............................................................................................................................................ 47
3.3 Methods .................................................................................................................................................. 50
3.3.1 Specimen: ................................................................................................................................................... 50
3.3.2 Measurement and Sensor Placement: ....................................................................................................... 51
3.3.3 Elicitation of Excess Correlation: ................................................................................................................ 52
3.3.4 Statistical Methods :................................................................................................................................... 55
3.3.5 Geomagnetic Data Extraction : .................................................................................................................. 56
3.4 Results: ................................................................................................................................................... 57
3.4.1 Electrophysiology of Excess Correlation : .................................................................................................. 57
3.4.2 Relationship between geomagnetic fluctuations and Excess Correlation: ................................................ 61
3.5 Discussion: .............................................................................................................................................. 62
3.6 References : ............................................................................................................................................ 66
CHAPTER 4 ............................................................................................................................................. 69
4 PASSIVE EXCESS-CORRELATION IN WATER ....................................................................... 69
4.1 Abstract .................................................................................................................................................. 69
4.2 Introduction ............................................................................................................................................ 70
4.3 Methods .................................................................................................................................................. 72
4.3.1 Measurement Apparatus: .......................................................................................................................... 72
4.3.2 Magnetic Field Configurations: .................................................................................................................. 73
viii
4.3.3 Excess Correlation Selection Criteria: ........................................................................................................ 76
4.3.4 Statistical Methods: ................................................................................................................................... 76
4.4 Results .................................................................................................................................................... 77
4.4.1 Raw Time Series Data: ................................................................................................................................ 77
4.5 Discussion ............................................................................................................................................... 82
4.5.1 Differences between sources: .................................................................................................................... 82
4.5.2 Experimental Phase-shift of DC voltage Inflection: .................................................................................... 82
4.5.3 Water Dynamics: ........................................................................................................................................ 84
4.5.4 Further Quantitative Support: ................................................................................................................... 86
4.6 References: ............................................................................................................................................. 90
CHAPTER 5 ............................................................................................................................................. 93
5 INTERACTION OF BULK ANGULAR VELOCITY ELECTROMAGNETIC FIELDS AND
TEMPERATURE VARIATIONS ON THE LATENCY OF PH SHIFTS .......................................... 93
5.1 Abstract .................................................................................................................................................. 93
5.2 Introduction ............................................................................................................................................ 93
5.3 Materials and Methods ........................................................................................................................... 95
5.3.1 Field Parameters and Exposure Apparatus: ............................................................................................... 96
5.3.2 Modulation of Temperature Variables: ..................................................................................................... 97
5.3.3 Statistical Methods: ................................................................................................................................... 97
5.4 Results .................................................................................................................................................... 98
5.5 Discussion ............................................................................................................................................. 102
5.6 References : .......................................................................................................................................... 107
CHAPTER 6 .......................................................................................................................................... 109
6 QUANTITATIVE SUPPORT FOR WATER AS THE CONDUIT OF INTERACTION
BETWEEN THE IMMATERIAL (ENERGY) AND MATTER : THE NECESSARY
CONTINGENCIES FOR UNIVERSAL EQUIVALENCE ................................................................. 109
6.1 Basic Properties of Water ...................................................................................................................... 111
6.2 Casimir and Electromagnetic Interactions ............................................................................................. 114
6.3 Gravitational Considerations ................................................................................................................. 117
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6.4 The Exclusion Zone ................................................................................................................................ 120
6.5 Photon-Graviton Entanglement in Water .............................................................................................. 122
6.6 References: ........................................................................................................................................... 125
CHAPTER 7 .......................................................................................................................................... 128
7 CONCLUSION .............................................................................................................................. 128
x
List of Tables
Table 2.1: Raw intensity measures intensity along the various distances of stimulation. Converted values
from intensity in lux to power density have also been provided. ................................................................ 26
Table 2.2: The combined results of a series of analyses of covariance (ANCOVAs) for presented variables
with photoperiod (on-off cycle) and intensity as between subjects measures with time of observation as a
covariate. Here S1 and S2 represent the relative age of the leaves green and senesced leaves respectively.
SD is the standard deviation of the change in potential difference between S1 and S2 leaves. All other
measures constitute the mean of the change in potential difference. Only the significance of the two-way
interaction is presented. .............................................................................................................................. 30
Table 2.3: Results of the factor analysis demonstrating the combined sources of shared variance. Here S1
and S2 refer to sample leaf type either green or senesced leaves respectively. .......................................... 31
Table 2.4: Multiple regression analysis results. Here the variables that are trying to be predicted are the
mean of the change in potential difference (DEL) for each sample, green and senesced (S1 and S2
respectively) with their temporal equivalents of testing. Variables entered that are represented of an SD
followed by a number is the standard deviation of the change in potential difference between samples
along the temporal continuum. All significant values are reported such that p<.001. ................................ 34
Table 3.1: The identification of the experimental parameters with appropriate point durations and
accelerating/decelerating rotating magnetic field configurations. .............................................................. 53
Table 3.2: Presentation of the differences in the magnitude of the correlation coefficients as a function of
species, field presentation and point duration. All values presented here are compared to controls of the
same species during the first 8 minutes of the second field. The sign of the z statistic denotes the direction
of the interaction as the computation performed was as such that the value of the field exposed groups
were subtracted from the values obtained in sham conditions. ................................................................... 61
Table 4.1: Representation of the field application series and associated point durations ........................... 75
xi
Table 4.2: A representation of the time-course of the experiment and the associated identification of
appropriate time bins. All values associated with labeled bins represent time during experiment in
minutes. Only those field combinations which have met the pre-defined criteria are represented in this
table along with sham condition. This table is complimentary to figure 4.1. ............................................. 77
xii
List of Figures
Figure 2.1a) – d) left to right and top to bottom display the serial organization of the days of testing
ranging from weeks 1 to 4. The data presented in the graphs represent the 4-way photoperiod, by
intensity, by leaf type by week interaction. Bars represent the mean of the change in voltage of all the
plants measured during their respective weeks of observation. Error bars are representative of the standard
error of the mean. The x-axis represents the distance (in cm) away from the light source. Conversions for
the appropriate intensity are located in Table 2.1. ...................................................................................... 27
Figure 2.2 a - d left to right and top to bottom representing the threshold phenomenon representing weeks
1 thru 4 respectively. Dark grey bars represent green leaves while light grey bars represent the senesced
leaves. Values displayed include the overall mean of the change in the potential difference between
specimen. The values of the threshold have been discussed in the previous section. Error bars signify
standard error of the mean. ......................................................................................................................... 29
Figure 2.3: Distribution of discriminant function group centroids for classification accuracy. Each
function along the x and y axes represent the combination of distinct variables that contribute to the
overall discrimination of the appropriate weeks of testing. In this case the linear combination of discrete
variables, defined in equation 2.1 and 2.2, the overall contribution to the appropriate differentiation of the
weeks of testing is apparent. The overlap between the computed centroid functions (representative of the
mean of the computed discriminant function equations) demonstrate a conspicuous sharing of variance
that does not fully distinguish between independent stages of senescence. Here centroid functions
demonstrate the geometrical combination of computed function variables and the associated triangulation
of the arithmetic mean of the combination of group classification values. ................................................. 33
Figure 3.1: A diagrammatic representation of the experimental apparatus. Abbreviations are as follows
DMM: digital multimeter, M.C: microcontroller. ....................................................................................... 54
xiii
Figure 3.2: An example of the raw electrophysiological profile of local and non-local interactions.
Downward arrows demonstrate when injections were made, and upward facing arrows denote when the
fields were applied. ..................................................................................................................................... 57
Figure 3.3: The non-specific effects of the applied magnetic field conditions on the degree of correlation
between local and non-local sites. Error bars represent calculated standard error measures. ..................... 58
Figure 3.4: The nature of the effects on BACTERIA as a local source are demonstrated in the left (A) and
right (B) graphs. The general trends represent the application of the different field geometries for both 1
(A) and 3 (B) msec respectively. Error bars represent the estimated standard error of the correlation. ..... 59
Figure 3.5: : The nature of the effects on PLANTS as a local source are demonstrated in the left (A) and
right (B) graphs. The general trends represent the application of the different field geometries for both 1
(A) and 3 (B) msec respectively. Error bars represent the estimated standard error of the correlation. ..... 60
Figure 4.1: Photograph of the sensor configuration and accompanying toroid. ......................................... 73
Figure 4.2: The representation of the experimental set-up. Here the abbreviations are as follows B1;
beaker 1, B2: beaker 2, M.C; microcontroller, DMM: digital multimeter. ................................................. 75
Figure 4.3: Raw outputs of the DC potential of local and nonlocal sources. The example above is one that
represents the 3 ms forward field presentation. .......................................................................................... 78
Figure 4.4 a-c : Top left hand graph represents the 3 ms field presentation, top right hand graph represents
the 1 ms field presentation series whilst the central bottom figure represents the control conditions. All
values presented in the graphs above consist of mean Z-score values within each condition. ................... 80
Figure 4.5: Demonstration of the effective application geometries as compared to control conditions.
Error bars reflect calculated standard error measures. ................................................................................ 81
Figure 4.6: Representation of minute by minute trends in Pearson correlation coefficients with respect of
differential application geometry. ............................................................................................................... 82
Figure 5.1: Room temperature events. In this condition the effects of 3 ms decelerating field was most
effective to hold pH over time while 1 ms accelerating field reduced overall shifts in pH after 9 minutes.
xiv
Error bars are measures of SEM. ACC denotes accelerating field parameters whilst DEC represents the
decelerating field parameters. ..................................................................................................................... 99
Figure 5.2: Cold condition, approximately 0⁰C, demonstrative that 1 ms accelerating field reduces the
overall change in pH over time. Error bars are measures of SEM. ACC denotes accelerating field
parameters whilst DEC represents the decelerating field parameters. ...................................................... 100
Figure 5.3: : Hot condition, 1 ms accelerating field effects are reduced and holding occurs for only 2
minutes. Error bars are measures of SEM. ACC denotes accelerating field parameters whilst DEC
represents the decelerating field parameters. ............................................................................................ 101
1
Chapter 1
1 Introduction to the Blueprint for Immortality
Immortality, at least from the human perspective, cannot seemingly be obtained. This
idea has pervaded the themes of many novels, forever etched in the concepts of the unattainable
fountain of youth. Furthermore, the application of the ability to exist in a suspended state,
roaming the lands feasting on living flesh has been the apocalyptic finale of existence on the big
screen. Although fantasized, to obtain a solution capable of reversing the causal nature of time
and extend our existence to an era which surpasses the limits of the human boundary would be
quite advantageous. However, the concept of immortality is merely a White Whale created to
divert us from the inevitably of death; the complete dissolution of our being. Although the
abstract concept of immortality may demonstrate juvenile intentions, perhaps one may be able to
reify the idea.
Fantasy aside, immortality is nothing more than the continued propagation of a set of
parameters across an unbounded magnitude of time. The central dogma associated with the
reverie of a non-realistic immortal existence suggests that a singular unit can exist for eternity.
Yet we, as an aggregate of individuals, fail to acknowledge the persistence of many such units
that adhere to the aforementioned definition of immortality. Take for instance the biological
aspects for the inheritance of genetic information. Genes, the unit of inheritance, are passed from
both a maternal and paternal biological source in order to form a subsidiary being, the offspring,
the entity that is you. Now postulate on the origins of your immediate relatives, your biological
sources, and posit the notion of their existence. Where did they come from? They are nothing
more than the combination and recombination of the genetic information (or the re-structuring of
2
the genetic units) of their biological sources. Reiterate this process any number of times over
successive generations and you may begin to realize that regardless of the number of
permutations, you are comprised of successively less genetic information of your ancestors.
Alternatively, your ancestors comprise a relatively small proportion of your genetic constituents.
Thus one may argue that, in a sense, a part of them has survived indefinitely, far exceeding the
limits of a single life, and has obtained a form of immortality. Even if we acknowledge the rules
of inheritance small portions of your genetic code still resemble past and distant relatives (Tyler,
2005). Thus, we have identified a singular aspect of immortality, a component if you will, that is
based solely upon the continuity of a given structure of a material nature.
The material can be defined as the structural analog of energy occupying both space and
time and composed of discrete units of matter. Matter is said to change very slowly over time
and maintain a consistent level of organization (Stachel and Toretti, 2002). Organization is an
emergent property of discrete units that coalesce into remarkably complex geometries. The
biological equivalent of the emergence of complex geometries derived from discrete units is the
structure of DNA. The composition and order of paired DNA bases are the foundations of genes,
the representative units of inheritance. There is a limit to the influence of the structure of a
solitary unit, and as the number of permutations increases over the course of successive
generations so too does the complex assemblages of geometries resulting in a decrease in the
influence of initially inherited information. The re-organization and recombination of initial
assembled geometries (i.e. distant genetic inheritance) can have unique interactions resulting in
highly variable final arrangements (individuals). In this discussion we have neglected an
important aspect of material, its ability to maintain structure and retain a process. In the
aforementioned sense, even if we received equal contributions of genetic material from our
3
biological sources, the combinatorial interactions that result from the initial genetic donors
produce completely new (individual) genetic structures. Our ideation of true immortality as
conceived by the propagation of a single unit (referred to as the self) cannot be obtained based
upon structure. An alternative property to structure (matter) is that it is finite, it persists over long
periods of time and it has the unique ability to represent a process.
Consider for a moment the concept of a cable wire conducting electricity. The example of
how electricity is conducted in said wire is often approached as the flow or movement of
electrons passing from one end of the wire to the other (Nitzan and Ratner, 2003). The metaphor
is erroneous as the material that makes up the wire, primarily a metal or metal-alloy, contains a
series of electrons that extend the entire length of the wire. In this case the electrons are bound to
adjacent atoms whilst some remain free-floating or unencumbered by the adhesion forces of
atoms. The application of an electromotive force then pushes one free electron off of the distal
end of the wire while simultaneously another enters the proximal end. The unencumbered
electrons then bounce into another free floating electron in a Grötthus-type mechanism and
allows for the generation of electricity. Still this example operates under an erroneous pretence.
Electrons in a material do interact in a Grötthus-type mechanism however the calculated velocity
at which these electrons travel, their drift velocity, is extremely small and would not account for
the immediacy in which one receives electrical power after inserting a device into an outlet.
What then can explain this phenomenon?
Consider for an instant the effect of applying a voltage over a given distance. The
resultant physical representation of said force can then be described as an electric field. The
concepts and behaviours of such emergent phenomenon have been extensively examined by
Faraday and modeled by Maxwell in his mathematical derivations (Maxwell, 1904; Maxwell,
4
1865). An electric field can be defined as a unit force acting on a unit charge and can have either
static (non-moving) or time-varying (dynamic) parameters. For our analogy, the current (flow of
electrons generated by their drift velocity) generates a movement of charge, a process which
assumes a temporal dimension occurring over finite increments. Returning to our cable
metaphor, the addition of a time-varying electromotive force generates a level of dynamism. In
turn the resultant application of a dynamic electromotive force produces a time-varying electric
field allowing for the immediate extraction of the necessary power from the overhead-wire, to
the breaker of your home, to the outlet and finally to the desk lamp that you have conveniently
turned on. Thus, it is the result of the electric field and its vector that allows for the necessary
properties associated with electrical conductivity in this example.
What, pray tell, does the cable do then if it is not directly involved with the conduction
and propagation of electricity in the common sense? The answer: cables provide structure to the
electric field. Any electric field, generated from a single charge, will radiate outwards
isotropically in all spatial directions producing a net equilibrium state (Plimpton and Lawton,
1936; McLaughlin, 1989). When we structure a series of charges in a bounded system (the wire)
and we apply a current in a single direction, we structure the resultant electric field such that its
vector lies parallel to the vector of the applied current. Thus the structure of the material adds
structure to the field, and the field functions in this case to supply a given target with a form of
energy. The aforementioned dichotomy has been immortalized in the concept that structure
dictates function.
We return now to our genetic structure and its unit of the DNA double-helix. The
structural result of the combination of these base chemicals allows for the delocalization of
electrons, similar to the unencumbered electrons in our wire. The net effect of the delocalized
5
electrons is the generation of static and dynamic electric fields. Similarly, DNA itself is not static
and undergoes structural changes such as compression, rotation, stretching, amongst others
(Bustamente et al., 2003; Kosikov, 1999). These conformational activities are the result of the
dynamic nature of the cell and its associated cytoskeleton leading to mechanical forces in the
picoNewton range that thus change the nature and movement of the delocalized electrons (Kubar
et al., 2008; Walert and Zakrzewski, 2005). The net electric flux associated with the structure of
DNA helps amplify the electric field associated with the static organization and distribution of
free electrons along its backbone, resulting in the production of a dynamic, time-varying electric
field. Complimentary to this aspect, any other physical changes to the structure of the DNA,
either during transcription, translation, gene regulation, expression, DNA damage etc., will
change the nature of the resultant electric field. Thus far, all the presented information has an
underlying theme – the importance of structure defining the nature of the emergence of function.
This pre-supposition has ushered in an era examining the importance of structure and material
but has discounted the reverse hypothesis. What if function also influenced structure? What then
would the influence of structure have on function?
Contemplate the question: What is a human? No more than the composition of calcium
based salts, bounded by a semi-permeable membrane in a colloidal suspension. However, one
cannot take the components of a human being in their respective proportions put them in a
beaker and generate a whole human as the alchemists would believe. In presenting this solution I
have not violated the idea that structure is an important factor in generating life. Yet the result of
the Gedanken experiment is still the same, it does not result in the manifestation of a human in
the beaker. What merely is left is approximately 70 kg of a solution containing a mixture of salts
6
and proteinaceous material in suspension. Perhaps a functional process is a necessity for the
generation of structure and is a requirement for the genesis and protracted assemblage of life.
Under the assumption that a process is required in order to initiate the appropriate
assemblage of subunits required to generate prototypal life, Miller and Urey conducted a series
of investigations examining the potential of electrical discharges affecting the composition of
gases akin to the constituents present on primordial Earth (Miller et al., 1976). What was
determined was that under the right conditions the discharges resulted in the synthesis of a
number of the essential amino acids, the basic building blocks of life. The generation of
biological subunits from inorganic material was coined abiogenesis and successfully
demonstrated the potential need of a process to organize the structure of matter (Huxley, 1870;
Hudgson and Baker, 1967).
In this manner structure can arise from a process, but what is the process of an electrical
discharge? Much like the lightning present during the abiogenic events of the primordial Earth,
electrical discharges result when the magnitude of spatially separated charges and the generated
electric field exceeds the dielectric constant of the material in which it is generated (Rakov and
Uman, 2003). This discharge occurs over a relatively short period of time, again resulting in the
generation of dynamic electric fields. One could postulate the necessity of a process as being
equally important to maintaining and generating the structure as is the structure in maintaining
the order of the process. This raises an interesting concept, that the electric fields or
electrodynamic processes of organized material are essential to the proper functioning of the unit
and may even precede the material organization in terms of necessity for the formation of the
unit.
7
Such a theory was proposed in the 1930's by Harold Saxton Burr and further studied by
Leonard J. Ravitz, Jr. into the 1960's. The proposed theory suggested that the characteristics of
the electric fields generated by a multitude of organisms were a pre-requisite for the nature of the
organism to be maintained (Burr and Northrop, 1935; Burr and Northrop, 1939; Burr, 1932;
Northrop and Burr, 1937; Burr, 1944). This proposal was coined the electrodynamic theory of
life and postulated that changes in the electric field of the organism, measured as slow shifts of
electrical potential, maintained the structure of the material. That is the emergent force, the
electric force from the electric field, acted as a binding agent and proverbial glue necessary to
bind and maintain the functional aspects of the material. Furthermore, any change in the
background quasi-stable nature of the intrinsic electrodynamic profiles of different organisms
could be used to assess aberrancies in the normal, operational parameters of the organism (Burr,
1941; Burr and Hovland, 1937; Burr, 1940; Burr, 1952; Ravitz, 1959; Ravitz, 1953; Ravitz,
1955; Ravitz, 1962). Burr and Ravitz also postulated that changes in electrodynamics preceded
the restructuring of the material with respect to wound healing and associated these measures
with the differential stages of embryological development (Burr and Hovland, 1937; Burr, 1941;
Ravitz, 1959). Furthermore, depending on the stage of development, the embryo would
demonstrate conspicuous alterations in the polarity (direction) of the electrodynamic vector.
Recent studies corroborate these findings and even suggest a role of polarity in the migration and
orientation of neuronal populations undergoing development (Yao et al., 2008, Yao et al., 2009).
Levin and colleagues examined the nature of electrodynamic correlates in directing
planarian regeneration (Oviedo et al., 2010; Nogi and Levin, 2005; Beane et al., 2011; Lobo et
al., 2012). The concept employed is based around the essentials of polarity, or the orientation of
charges in a specific direction. The net result of all charges produces a polar separation of
8
charges creating a net positive and net negative segregation. The separation and maintenance of
these polar regions can be accommodated by the generation and temporally bounded effects of
electric fields and thus can be inferred as the causal agents of electrodynamic field effects. The
fact is the labels might change, the metaphors might evolve and the tools may become more
refined, but the underlying ideas do not differ from their original conception.
What might this have to do with the concept of immortality in the context which we have
described? We return to the man that started it all, Burr. Burr (1972) released a book entitled:
Blueprint for Immortality: The Electric Patterns of Life which chronicled his works and those of
others in the field of electrodynamics. The crux of the dissertation examined the slow changing
nature of plants, primarily trees, as they demonstrated an extended life beyond that of a human
life. In his book Burr suggested that the very nature of the electric fields generated the necessary
parameters in order to maintain life or coalesce the structures necessary to maintain life. An
inference of the work suggests the possibility of super-imposing distinct patterns of electric
fields from one organism to another which is composed of the same material. One must be
cautious at the notion that the same structures produce the same fields – they do not. Even
though organisms may be structurally homologous with each other they do not demonstrate
homologous electrical patterns. That is to say that the electric field patterns of a given organism
can be described as a fingerprint for that organism distinct from all that rest (Ravitz, 1955;
Ravitz, 1951). Unfortunately many of Burr’s hypotheses could not be tested as the technology at
the time was limited in its scope.
Burr's theory postulates that material interacts by way of an arbitrary boson, a unit of
information transfer. However, consider the alternative hypothesis which incorporates Burr's
postulate, of the effect of an appropriate applied magnetic field interacting with a structures’
9
electric field and ultimately influence/direct any underlying processes. Such a magnetic field can
interact with distant material in two ways: 1) propagations through a medium, or 2) through an
entanglement-like phenomenon. The first method, that of propagation, assumes that the intensity
of the magnetic field decreases as the square of the distance increases and is said to follow the
Inverse Square Law. This requires the materials to be within a proximal distance to each other
for any exchange to possibly occur. The second method, that which obeys the notion of
entanglement (a simultaneous alteration in the properties of isolated materials that cannot be
described by classical methods), is not limited by the reduction in intensity of the field in any
manner. We have considered that the magnetic and electric fields act as a carrier wave projecting
units of information. In the propagation method the information that is being transmitted is
reduced in its effectiveness (akin to magnitude) due to the depression of the amplitude associated
with this classical transmission technique (Inverse Square Law). In stark contrast, the
phenomenon of entanglement does not affect the clarity of the signal and its efficacy of being
transferred from one location to the next.
A field is a continuum of discrete tangible potentials that interact with the components
constituting space and time. This can be likened to a diffuse array of varying potentials
producing a background flux allowing for complex interactions. We have identified fields in this
context as either being magnetic or electric in nature. The effects of the application of these
fields are generally described as a wave. A wave can have two properties with regard to
information theory; it is either it is a carrier wave or a signal wave which exist as a
complimentary unit. A carrier wave is generally large in amplitude and low in complexity whilst
the signal wave is generally very low in amplitude with marked complexity. Here the purpose of
the carrier wave is to have a signal of a more complex nature (primarily temporally modulated or
10
phase shifted) embedded within it. It acts as a shuttle or package that accompanies the message
from sender to receiver. Conversely, the signal wave generally carries the necessary information
from the sender to the receiver. We have presented an idea that the resultant electrodynamic field
patterns (signal) subsequently generate magnetic field equivalents containing both the properties
of carrier and signal. This allows the magnetic field to act as both an initiator of entanglement
(i.e. displays the appropriate parameters necessary to induce the correlates of entanglement) or
participate as a carrier wave through propagation and classical transmission mechanisms. The
activities of these fields are not limited by our functional definitions and can demonstrate
multiple properties simultaneously.
The phenomenon of entanglement has been explored in relativistic and quantum
mechanical means and can be rationalized such that if you have two particles (e.g. electrons,
protons) that are entangled any change in one unit will result in a proportional and inverse
change in the other unit even though the two systems are isolated and separated by vast distances
(Hill and Wootters, 1997; Bell, 1964; Polchinski, 1991). Given that this scenario is possible, the
alteration in the information (field dynamics) of one material may indeed affect the same
material simultaneously regardless of the degree of separation. From our previously identified
equations, as derived by Maxwell, the application of a magnetic field can alter the
electrodynamic properties (time-varying electric field component) through a Faraday induction
effect and thus alter the characteristics, the electric pattern, of a given substance. This indirect
application of Burr's hypothesis may substantiate the claims of super-position of electrodynamic
patterns between similar materials. What is required, however, is not simply the sceptical
application of theory but the direct observation and measurement of serial manipulation. Thus we
propose a series of experiments examining the nature of immortality, and the possible application
11
parameters necessary to alter the structural arrangement of the organism and a potential
investigation into the super-position of electrodynamic states between similar and dissimilar
material and the limits of this application.
If one wishes to identify the origins of the Blueprint for Immortality one must understand
the nature of how the primary building blocks of our existence interact. The physical perspective
of the genesis of living material commenced at the initiation of the Big Bang. From this moment
the energy of the Universe expanded outwardly forming pockets of plasma (Copi et al., 1995).
These high energy areas gradually cooled and amalgamated into the fundamental building blocks
of our Universe, the various atomic elements. Classically, this explanation is proposed to
accommodate how atoms are formed and from that our inevitable genesis. However, consider an
alternative, one where matter is strictly the necessary constituent to bind energy and contain it
over larger temporal spans. Energy itself can interact with material. It can be transformed from
one form to the next but ultimately it is insubstantial. Matter, in contrast, has permanence to it.
Matter allowed the exchange of energies to occur across space time and to become stored. In
order for any process to evolve, matter must have been formed from energy. It is suggested that
energy itself is the Blueprint, or at least holds the Blueprint, for Immortality. It is the transition of
energy (insubstantial) to matter (substantial) which denotes the initial genesis of prototypical life.
Studying the nature of matter in its fundamental forms should then provide insight to the
Immortal nature of energy.
It is important to determine the nature of the fundamental constituents of our Universe
and this endeavour is well underway. Similarly, in order to understand the origins of our
Blueprint and examine the Immortal potential that we may possess, we must begin with our
12
genesis. I have discussed the origin of our genesis with regards to the Miller-Urey studies
however we have neglected a key constituent of the experiment: water.
The properties of water are suggestive of a complex nature of activity reflecting the
necessary parameters to encourage the directed genesis of life. We generalize the term life to
include all forms that contain a cell membrane. The potential for a primordial precursory
membrane has been hypothesized and its origins traced to the structure of water. Trevors and
Pollack (2011) hypothesized that radiant energy in the infrared portion of the spectrum results in
a charge differential of 100 to 200 mV and is associated with the genesis of pre-biotic cells. The
hypothesis presented in the current document was based upon the observations of Chai and
Pollack (2010) which demonstrated a maximum expansion of the exclusion zone (EZ) of water
when exposed to incident radiation of 3.4 μm wavelength. The exceptional property of water to
generate EZ’s (i.e. solute free zones) around a surface supports the hypotheses of water having
the potential to generate impermeable or semi-permeable regions akin to membranes. The idea of
water as the precursor to the formation of biological membranes is predicated on the generation
of these solute free zones. Magnitudes of electrical potential of the exclusion zone is well within
the range of the depolarization of neurons (Kandel and Spencer, 1961) and reflects the
magnitude of the resting membrane potential of some bacterial and animal cell types (Hodgkin
and Huxley, 1952; Hille, 2001).
Further evidence supporting the hypothesis of water being a precursor cell membrane is
provided by Murugan and colleagues (2013). When samples of spring water were stored in a
dark environment (18 d) and exposed, for the same duration, to 1 μT physiologically patterned
electromagnetic fields which were complimented by complex frequency modulations, a reliable
shift in peak fluorescent wavelength of 10 nm was observed. These results demonstrate the
13
potential for appropriately patterned magnetic fields to displace the energetic equivalents by
about the size or thickness of a cell membrane. Furthermore, Zheng and colleagues (2006)
measured T2 relaxation times of bulk and interfacial water using MRI imaging techniques. It was
determined that a value of 27.2 ± 0.4 msec and 25.4 ± 0.1 msec were identified for both bulk and
interfacial water respectively. The latter value corresponds to findings determined by Murugan et
al. (2014) and reflects a level homogeneity associated with the rostral-caudal depolarization time
most commonly correlated to consciousness.
In this dissertation we outline a series of experiments that honour the electrodynamic
postulates originating with Burr and to extend the breadth of knowledge using more refined
equipment. In our first experiment we examine the electrodynamic changes in plants as they
undergo transition into their senesced state. Generally, senescence is a state of quiescence that
annual plants undergo upon transition into the winter months. This particular investigation
serially examines transitional phases over short periods of time in order to identify the
electrodynamic correlates associated with maintaining the necessary structure to extend the life
of the organism. Our second approach examines the possibility that dissimilar materials sharing a
process can in fact demonstrate similar electrodynamic properties. This latter investigation, if
possible, eliminates the constraints applied by Burr in his original hypothesis. It may usher in the
opportunity to store electrodynamic patterns in a vessel not necessarily effected to the same
extent of a human shell to the progressive nature of time. In this context we operate under the
assumption that if plants and bacteria share a potential candidate for the Blueprint of Immortality
then they should interact in a meaningful way even though they may not possess the same
genetic or structural organizations. Our penultimate experiment examines the possible
application of entanglement-like phenomena as a means to exert a change in the electrodynamic
14
characteristics of similar material. In this context we examine the passive exchange of electrical
patterns in an attempt to synthesize Burr's far-reaching ideas and explore the important role water
may play in it. Our final experiment examines the interaction of temperature variations and
electromagnetic fields on the unique properties of water in order to understand the dynamic
nature of the initiator of our existence.
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18
Chapter 2
2 Electrophysiology of the Dynamic Profiles of Plants Undergoing
Senescence
2.1 Abstract:
DC voltage changes of whole sedge plants (Carex stricta) demonstrate conspicuous alterations in
recorded electrophysiological profiles whilst undergoing senescence. We demonstrate time-
dependent changes contingent upon the seasonal variations of environmental variables as
measured by whole plant electrodynamics. Furthermore, plants responding to applied light
demonstrate three peak intensities capable of eliciting significantly different changes in voltage
between plants. Quantitative support for threshold of light stimulation converges at the level of
the cell and demonstrates a quantum-cell equivalent. The implications of water and charge for
maintaining membrane dynamics are also explored.
2.2 Introduction:
2.2.1 Physiological Perspectives of Senescence
Senescence is a complex biochemical and physiological process as the final stage of the
plant life cycle (Lim et al., 2007). Although the result of the transition from one stage of
development to another is associated with an intricate variation in the total organism, senescence
has been limited to the examination of physiological changes and their associated alterations in
protein production or degradation (Gan and Amasino, 1997). The study of senescence has been
categorized into two groups of observation reflecting the distinct structural components which
are intended to be examined. The first approach to understanding senescence is accomplished by
examining the change in leaf structure and chlorophyll composition and other biochemical
19
alterations over time. Subsequent to the analysis of leaf dynamics, changes associated with the
progression and alterations in the apical meristem constitute the second categorical observation
of senescence (Hensel et al., 1994). The respective investigations of leaf and meristematic
associated senescence are considered post-mitotic and mitotic senescence, respectively (Gan,
2003).
In this particular instance, the examination of both mitotic and post-mitotic senescence
can be translated into whole plant changes. For instance, degradation in leaf chlorophyll
composition alters the efficacy of photosynthetic processes (Dickinson et al., 1991; Bing et al.,
1993) which can affect other processes that are dependent upon the products of the
photosynthetic cascade. The reduction in photosynthesis initiates for the mobilization of
macromolecules to storage areas situated in appropriate tissues of the plant (Thomas and
Donnison, 2000; Zavaleta-Mancera et al., 1999; Buchanan et al., 2003).
Alterations occurring at the meristematic region, an area which is associated with high
growth rates, slow in order to accommodate such a large scale change (Rolland et al., 2006). The
slowing and eventual cessation of meristematic area activity reduces the overall energy
consumed by the plant or a redistribution of said energy. Reduction and cessation of growth
results in two phenomena: an increase in available energy and cellular arrest. In the latter case
cells arrest their differentiation and maintain a prolonged state of inactivity (Havel and Durzan,
1996). Cell cycle arrest allows for the structural equivalent of the cell to be maintained until the
environment contains the appropriate variables to re-initiate and sustain prolonged regular
cellular activity (Bleecker and Patterson, 1997; Lim et al., 2007).
Plants demonstrate the ability to undergo both post-mitotic and mitotic senescence (Gan,
2003) which may in turn impact the electrical potential of the plant proper which is supported by
20
the changes in the electrochemical properties of the plant as recognized by Davies (2004). From
a leaf senescence perspective, physical changes in the plant result in the marked degradation of
available chlorophyll in a reliable manner (Lim et al., 2007). It is suspected that changes in
electrochemical potential can be translated into alterations in the electrical potential of the entire
plant (Fromm and Lautner, 2007; Fensom, 1957; Zimmerman et al., 2009).
2.2.2 Electrophysiological Perspectives: The possibility of using light-mediated alterations
to affect electrophysiological responses in plants in order to assess differential stages
of senescence
Ultrastructural alterations within the complex cellular matrix of plant biology provide
valuable information with respect to the structural alterations that accompany senescence.
However, the method of ultrastructural, cellular investigation into senescence does not provide
dynamic information throughout the progression of senescence to its termination. Primarily,
these investigative techniques rely on measuring static, completed changes and reveal no
information into the time-course of the process. Furthermore, despite the specificity of genetic
and histological analyses, they do not produce a full picture of the sum of the changes that occur
with the entire plant. In light of such limitations, the possibility of employing a dynamic
measurement to assess the transient alterations within the entire plant as it undergoes structural
alterations associated with senescence may provide alternative insights.
One potential candidate which can accommodate such dynamic measures would be the
use of direct current (DC) potential differences over a given distance. DC potential is an
emergent property of the aggregate organization of electrogenic plant tissue, orienting the flow
of current within the plant ultimately generating an electric field around the organism (Nuccitelli,
1984). Physically, if an electric field is applied over a given distance (i.e. the distance between
two sensors of a voltmeter) a potential difference results. Masi et al. (2009) utilized a 60
21
microelectrode array to study the spatiotemporal electrical activity at the root apex and found a
pattern in which spikes of electrical activity developed spontaneously. These spontaneous
electrical spikes formed synchronous, transient episodes that were followed by epochs of no
activity which lasted many seconds. This demonstration of electrical spike activity may lend
credence to the hypothesis that plant action potentials generated in electrically excitable cells
may be linked to processes such as photosynthesis and respiration (Fromm and Lautner, 2007;
Lautner et al., 2005).
Supplementary support for the use of electrical measures in plant research were provided
by Gurovich and Hermosilla (2009) who used continuous measures of electrical potential in
plants to identify changes in responses associated with alterations in local environmental
variables (temperature, humidity, moisture, water availability, light and dark exposure, etc.). The
determination of a complex relationship between local variables and electrical potential
difference was identified aiding the notion of whole plant electrical alterations as an effective
means of assessing information on the responses of the organism. Specifically, changes
associated with light and dark conditions displayed alterations in profiles of potential difference
(Gurovich and Hermosilla, 2009) whose activity may be related to membrane depolarizations
linked to photosynthetic processes (Whitmarsh, 2004). Szechyńska-Hebda and colleagues (2010)
corroborated these ideas by demonstrating that photoelectrophysiological signals could be
generated by altering the amount and duration of light exposure. They concluded that plant
action potentials (ranging from 15 to 50 mV in intensity) occurred within seconds of the initial
stimulus and could be related to many physiological processes (Szechyńska-Hebda et al., 2010).
The time domain, in the order of seconds, may correspond to slow conduction velocities or may
represent an optimal spatial relationship. Felle and Zimmerman (2007) concluded that biphasic
22
potential differences in barley samples corresponded to a conduction velocity of 20-30 cm∙min-1
whilst Masi et al. (2009) demonstrated conduction velocities in the order of 20 cm∙s-1 in maize.
Thus, in order for measures of potential difference to be effectively assessed, times frames of
seconds to minutes and distances in the order of tens of centimeters are required.
Examining electrophysiological measures over tens of centimeters and temporal durations
of seconds to minutes is corroborated and can indeed be used to assess whole plant alterations.
However, the question of the efficacy of such a technique to assess the degree of senescence
arises. Szechyńska-Hebda and Karpiński (2013) demonstrated the ability of plants to acclimate
to alternating light intensities and linked said alterations to photo-electrochemical potential (a
form of induced voltage change) mediated by chloroplast signalling. Relating potential
difference to senescence, the onset of leaf senescence is related to a decline in available
photosynthetic proteins and associated activities (Szechyńska-Hebda and Karpiński, 2013).
Furthermore, the initiation of senescence is contingent upon the intensity of available light rather
than quality (Szechyńska-Hebda and Karpiński, 2013) and involves the degradation of
chloroplasts (Pogson et al., 2008; Pfannschmidt, 2010). Thus a change in the integrity of the
chloroplasts alters the electrochemical gradient resulting in changes in the potential difference of
the plant that can be transmitted along the bundle sheath throughout the organism (Szechyńska-
Hebda and Karpiński, 2013). Any compromise to the chloroplasts or photosynthesis-associated
proteins will change the degree of response to, and the efficacy of the incident light’s ability to
activate, the appropriate pathways which in turn will affect the generated potential difference
across the plant. Is it then possible to assess the degree of senescence of a plant based upon the
electrical responses to cyclic presentations of light and dark?
23
2.3 Methods:
2.3.1 Measurement devices
Measurements were conducted using two independent Victor 70C direct current (DC)
digital multimeters, two independent laptop computers and associated recording software. The
laptop monitoring systems remained paired with their associated DC multimeters for the duration
of the experiment. There was no experimental measurement error associated with the port time
of the computer as the monitoring systems (digital multimeters) had matched sampling
frequencies and port time delays. Sampling rate was set at 10 sec increments.
2.3.2 Specimen
Plants of Carex stricta (tussock sedge; Cyperaceae, three replicates), originating from a
Sudbury area wetland were vegetatively propagated and grown in 10 L plastic containers that
were perforated at the base. These plants were grown for two years in an experimental garden 25
km north of the Laurentian University campus and were standing in 10 cm deep pools of ground
water. In the latter part of September 2014, these plants were moved to smaller pools of water
located on the grounds of Laurentian University where measurements were conducted between
the 8th of October 2014 and the 7th of November 2014. During this time leaves of this species
senesce as nutrients are mobilised into storage organs before the onset of winter (Ryser and
Kamminga, 2009; Figure 2A). Individual plants were tested once a week on the same night each
week for four weeks. On the day of testing, the selected plants were removed from their water
basin and placed in another basin containing 10 to 15 cm of water in the testing location. The
temperature in the test location was consistently held at 23℃ with ample light. All plants were
allowed to acclimatize to the testing room for at least 12 h prior to testing. Testing began
between the hours of 22:00 and 02:00 local time (EST). The duration of direct light exposure
24
ranged from 11 h 21 min (October 8th 2014) to 9 h 49 min (November 7th, 2015) during all
conditions and acclimation periods.
2.3.3 Sensor placement
Sensors, anodes and cathodes, were attached to the root and leaves of each specimen. The
anode (reference) was placed approximately 10 cm below the potting soil surface and was
attached by a silver-chloride flat disc sensor to a single root. The distal end of the flat disc sensor
was affixed upon the carrying handle to which each individual plant was housed. Both DC
multimeters were referenced to the distal end of the flat disc sensor. The cathodes (measuring
sensors) were attached to twodifferent samples of leaves on the plant. The criteria for selection
will be addressed in the following section. The two leaf samples were attached to their respective
monitoring devices by use of an alligator clip. The linear equivalent distance from the surface of
the potting soil to the alligator clip was approximately 10 cm, resulting in a total linear
separation between the sensors of 20 cm.
2.3.4 Leaf selection
Two leaf samples were selected in order to discern the differential progression of
senescence within an individual plant as measured by electrophysiological parameters. Leaves
were selected based upon their progressive loss of chlorophyll before the commencement of this
experiment. Here green leaves (S1 measures) were leaves that retained a bright green colour
along their entire length. If, however, there were no fully identifiable green leaves then the
criteria such that at least 80 % of the leaf retained its bright green colour was employed. All
plants met at least one of these two criteria for the S1 samples. Conversely, senescing leaves (S2
samples) had to meet the following criteria: complete absence of green pigmentation and/or
wilting with the presence of a discolouration (along the continuum between yellow and brown)
25
along the apical surface of the leaf. All S2 measures met these criteria before the initial testing
phase began.
2.3.5 Electrophysiological manipulation
Testing was conducted on individual plants once a week whose DC electrical potentials
were sampled once per 10 sec for the duration of the measurement period and repeated for the
four weeks of observation. Plants were monitored for changes in electrical potential (DC voltage)
along a concentration of light intensity (lux). The exposure paradigm consisted of a photo-
stimulus (light-on), photo-absence (light-off) cycle following a 2-min cycle length in an A-B-A-
B-A format. Gradient alterations in stimulus intensity were modulated by moving the source of
photo-stimulation away from the plant. The photo-stimulus source was a 40-W incandescent
light-bulb in a conical lamp positioned along a continuum between 20 and 200 cm. Each new
intensity was modulated by consecutive 20 cm linear displacements in the light source until a
distance of 200 cm was obtained. Raw values for the gradient of light intensity have been
included in Table 2.1. In this particular environment, the dissipation of heat from the stimulus
apparatus was negligible. All values for DC potential were recorded using the appropriate
software and raw recordings were imported into IBM SPSS 17 for statistical analyses and
accompanied data transformations.
26
Table 2.1: Raw intensity measures intensity along the various distances of stimulation. Converted values from
intensity in lux to power density have also been provided.
Distance (cm) Intensity (Lux) Power Density (W∙m-2)
20 1686 2.47
40 560 8.20∙10-1
60 255 3.73∙10-1
80 152 2.23∙10-1
100 95 1.39∙10-1
120 73 1.07∙10-1
140 49 7.17∙10-2
160 38 5.56∙10-2
180 29 4.25∙10-2
200 25 3.66∙10-2
2.4 Results:
2.4.1 The effects of intensity and photoperiodicity
The mean of the instantaneous change in voltage (the change in voltage per measurement
interval) was calculated across individual plants from either S1 or S2 leaves referenced to the
root and entered into a 4-way photoperiod by intensity by leaf age by week repeated measures
multivariate analysis of variance (MANOVA) and revealed a significant within subjects
interaction [F(27,693) = 3.64, p<.001, pη2 = 0.12]. The resultant 4-way interaction is presented in
Figures 2.1 A thru D. Multiple post-hoc one-way analyses of variance (ANOVAs) were
conducted in order to identify the origin of the interaction and determined that the source of the
interaction was due to the discreteness of the intensity of light that the specimen received.
Differential effects associated with the application of a light stimulus demonstrated a necessary
threshold of intensity for the effective alteration in the voltage of the whole plant. Results from
the post-hoc analyses determined a trifurcation of intensity (three peaks along the gradient of
light intensity) corresponding to linear equivalents of 20 to 40 cm (P1), 120 to 140 cm (P2) and
180 to 200 cm (P3). Figures 2.2A) thru 2D) represent the threshold phenomenon for these
27
particular specimen under this paradigm. These results are suggestive of essential peak
intensities that are necessary for the appropriate response of the plant. Furthermore, Carex stricta
undergoing changes in response to variable alterations in the local environment do not respond in
an intensity dependent manner.
Figure 2.1a) – d) left to right and top to bottom display the serial organization of the days of testing ranging from
weeks 1 to 4. The data presented in the graphs represent the 4-way photoperiod, by intensity, by leaf type by week
interaction. Bars represent the mean of the change in voltage of all the plants measured during their respective weeks
of observation. Error bars are representative of the standard error of the mean. The x-axis represents the distance (in
cm) away from the light source. Conversions for the appropriate intensity are located in Table 2.1.
28
2.4.2 Weekly variations in electrodynamic responses to peak intensities:
Further examination of the now defined peaks for each of the green leaves were
conducted to assess differences in the mean change in voltage of all plants corresponding to the
identified three peak intensities over time. Here the results of serial ANOVAs suggest that 6 out
of the 8 computed variables were different between the three peak intensities. Primary
differences in peak intensities during Week 1 for the green leaves were being driven by the mean
changes in voltage of all specimen associated with the first peak (P1, -0.29 mV) and the second
peak (P2, 0.30 mV) peaks. The mean change in voltage of the green leaves during Week 2 of the
paradigm for the second peak (P2) and the first peak (P1) intensities (-0.28 mV and -0.17 mV,
respectively) were significantly different from the third peak (P3) intensity (0.27 mV) [F(2,154) =
6.60, p<.01, Ω2-est = 0.08]. Week 3 differences were generated by the differences in mean
change in voltage of all specimen between intensities at the second peak (P2, -1.97 mV) and the
third peak (P3, 1.18 mV) intensities [F(2,154)= 3.31, p<.05, Ω2-est = 0.04]. Finally, for the green
leaves on Week 4, the driving force behind the interaction was the result of the difference in
mean voltage changes between the primary peak (P1, -0.77 mV) and both third (P3, -0.14mV)
and second peak (P2, 0.04mV) intensities [F(2,154) = 5.22, p<.01, Ω2-est = 0.06].
Senesced leaves did not demonstrate significant differences between the mean change in
voltage at the three peak intensities during Weeks 1 and 4. However, for Week 2 significant
differences were determined to be between the mean change in voltage of senesced leaves during
the first peak (P1, -0.10 mV) and the second peak (P2, 0.30 mV) intensities [F(2,154) = 3.10,
p<.05, Ω2-est = 0.04]. Lastly, Week 3 differences in the mean change in voltage of senesced
leaves at the third peak (P3, 0.08 mV) and the second peak (P2, 0.23 mV) intensities were
29
significantly different from each other and from the primary peak (P1, 0.43 mV) values [F(2,154) =
19.12, p<.001, Ω2-est = 0.20].
Figure 2.2 a - d left to right and top to bottom representing the threshold phenomenon representing weeks 1 thru 4
respectively. Dark grey bars represent green leaves while light grey bars represent the senesced leaves. Values
displayed include the overall mean of the change in the potential difference between specimens. The values of the
threshold have been discussed in the previous section. Error bars signify standard error of the mean.
Supplementary two-way analyses of variance with time as a covariate (ANCOVAs) were
conducted on the mean change in voltage and its associated standard deviation across specimens
for the appropriate stage of senescence (week of measurement) in order to discern the degree of
30
interaction between the photoperiod and intensity as a between subjects effect. A summary of
these results are presented in Table 2.2.
Table 2.2: The combined results of a series of analyses of covariance (ANCOVAs) for presented variables with
photoperiod (on-off cycle) and intensity as between subject’s measures with time of observation as a covariate. Here
S1 and S2 represent the relative age of the leaves green and senesced leaves respectively. SD is the standard
deviation of the change in potential difference between S1 and S2 leaves. All other measures constitute the mean of
the change in potential difference. Only the significance of the two-way interaction is presented.
Leaf type Week Sig. F-value ΔF Ω2-est. ΔΩ2
S1 1 ------------ 1.79 ------------ 0.057 ------------
S1 2 ------------ .087 -1.70 0.003 -0.054
S1 3 <.001 3.92 3.83 0.122 0.119
S1 4 <.001 6.12 2.2 0.149 0.027
S2 1 ------------ 1.38 ------------ 0.046 ------------
S2 2 ------------ .56 -0.82 0.021 -0.025
S2 3 ------------ 1.75 1.19 0.055 0.034
S2 4 <.01 2.35 0.60 0.087 0.032
SD 1 ------------ 1.14 ------------ 0.039 ------------
SD 2 <.05 2.07 0.93 0.072 0.033
SD 3 <.05 2.42 0.35 0.070 -0.002
SD 4 <.001 4.92 2.5 0.105 0.035
2.4.3 Common sources of variance:
To further the understanding of the effects observed in the electrophysiological changes,
the mean change in voltage for each leaf age (senesced and green) and associated stage of
senescence (Weeks 1 thru 4) were entered into a factor analysis. The primary reason for this
analysis was to examine potential shared sources of variance that may be associated with the
temporal (age of leaf) characteristics of electrophysiological parameters associated with the now
defined peak intensities. The mean change in voltage between specimens for the age of the leaf
and the intensity of the photo-stimulus were the variables that were used in this particular
analysis. The analyses generated 8 factors that contributed to a total variance explained of ~ 65%
with respect to age of leaf and intensity dependence. The results of the varimax rotated
component matrix (with a loading coefficient of 0.40 or greater) are presented in Table 2.3 along
with the individual contribution to the total explained variance of each factor.
31
Table 2.3: Results of the factor analysis demonstrating the combined sources of shared variance. Here S1 and S2
refer to sample leaf type either green or senesced leaves respectively.
Factor % Variance
explained
Component
Variables
Loading Coefficients
1 11.4 S1 at 80 cm
S1 at 120 cm
S2 at 160 cm
.638
.708
.842
2 9.7 S1 at 40 cm
S2 at 40 cm
.940
.910
3 9.2 S1 at 200 cm
S2 at 100 cm
.603
-.661
4 8.7 S1 at 20 cm
S2 at 140 cm
.466
.690
5 7.0 S2 at 60 cm
S2 at 200 cm
.786
.814
6 7.0 S1 at 20 cm
S1 at 140 cm
S1 at 180 cm
S1 at 200 cm
-.541
.549
.694
.493
7 6.3 S2 at 80 cm
S2 at 120 cm
.512
.807
8 5.5 S1 at 60 cm .633
2.4.4 Classification of distinct stages of senescence
A discriminant analysis was conducted on computed raw variables (with intact number of
cases) in order to identify the possibility of differential stages of senescence. Here max-steps
were set to five, and the variables that entered into the discriminant equation were the between
samples standard deviation of the relative change in voltage (SDREL), the root mean square of
the relative change in voltage (RMSREL), the squared relative voltage of both the green and the
senesced leaves (RELSQ1 & RELSQ2), and the relative change in voltage of the senesced leaves
(RELS2). The classification success of the discriminant analysis approached 74% accuracy based
on a cross-validated method [Χ2 (15) = 4054.406, p <.001, 1-λ = 0.77] (Figure 2.3).
Equation 2.1: Discriminant Function Equation 1 = -0.013∙(RELS2)+0.059∙(SDREL) -
7.2∙10-5 ∙ (RELSQ1) + 3.3∙10-4 ∙ (RELSQ2) - 0.025∙(RMSREL) -1.590
32
Equation 2.2: Discriminant Function Equation 2 = 0.016∙(RELS2)+0.027∙(SDREL) +
4.1∙10-5 ∙ (RELSQ1) + 8.9∙10-4 ∙ (RELSQ2) - 0.070∙(RMSREL) +1.789
Highest scores for the discriminant function equation were obtained for Week 4 (1.98)
whilst the lowest scores were obtained for Week 1 (-1.06). Weeks 2 and 3 demonstrated
remarkably similar discriminate function equation values for group centroids of -.56 and -.71
respectively. Upon examining the resultant classification statistics for each group membership a
divergence in the classification accuracy arises. Here the appropriate classification for Week 1
and Week 4 group memberships were 99.6% and 86.1%, respectively, however both Week 2 and
3 values were not as precise at separating measured electrophysiological parameters into distinct
group memberships. The overlap and reduced discrimination of Weeks 2 and 3 may suggest a
temporal evolution to the onset of senescence which can be approximated using
electrophysiological measures.
33
Figure 2.3: Distribution of discriminant function group centroids for classification accuracy. Each function along the
x and y axes represent the combination of distinct variables that contribute to the overall discrimination of the
appropriate weeks of testing. In this case the linear combination of discrete variables, defined in equation 2.1 and
2.2, the overall contribution to the appropriate differentiation of the weeks of testing is apparent. The overlap
between the computed centroid functions (representative of the mean of the computed discriminant function
equations) demonstrate a conspicuous sharing of variance that does not fully distinguish between independent stages
of senescence. Here centroid functions demonstrate the geometrical combination of computed function variables and
the associated triangulation of the arithmetic mean of the combination of group classification values.
2.4.5 Temporal Dynamics
Using the results obtained from our discriminant function a model of the temporal
evolution of senescence can be approached. Here the predictive model for the onset of
senescence was conducted using only the mean change in voltage between specimens and their
34
associated standard deviations of this measure across successive trials in a multiple regression
analysis. The results of the successive multiple regression analyses (Table 2.4) suggest, and
corroborate, that a temporal phenomenon associated with senescence exists.
Table 2.4: Multiple regression analysis results. Here the variables that are trying to be predicted are the mean of the
change in potential difference (DEL) for each sample, green and senesced (S1 and S2 respectively) with their
temporal equivalents of testing. Variables entered that are represented of an SD followed by a number are the
standard deviation of the change in potential difference between samples along the temporal continuum. All
significant values are reported such that p<.001.
Dependent
Variable
Variables
Entered
F-value
Adj. R2 Predictive Equation
DELS1T1 SD1,
DELS2T1,
DELS2T4
45.65 .349 -.328∙SD1 +.200∙DELS2T1 +
199∙DELS2T4 +.227
DELS1T2 SD2
DELS2T2
21.98 .144 -.385∙SD2 -.084∙DELS2T2 +.196
DELS1T3 ------------- ------------- ------------- --------------------------------------------
DELS1T4 SD4
SD3
SD1
DELS1T3
69.25 .522 -.975∙SD4 -.057∙SD3 -.258∙SD1
+.020∙DES1T3 +.373
DELS2T1 SD1
DELS1T1
DELS1T4
48.72 .364 .743∙SD1 + 1.253∙DELS1T1 -
.258∙DELS1T4 -.410
DELS2T2 SD2
DELS1T4
DELS1T2
11.02 .107 -.513∙SD2 +.120∙DELS1T4 -
.216∙DELS1T2 +.380
DELS2T3 ------------- ------------- ------------- --------------------------------------------
DELS2T4 SD4
SD3
DELS1T3
6.47 .062 -.179∙SD4 -.032∙SD3
+.018∙DELS1T3 +.222
2.5 Discussion:
2.5.1 Electrophysiological Correlates of Senescence
We have demonstrated that complex bio-electrophysiological changes in response to
alterations in periodic light application occur during the transition into a state of senescence.
First and foremost, Carex stricta plants, at least in regards to this particular paradigm, do not
respond to light in an intensity dependent manner. However appropriate peaks in intensity that
35
sufficiently alter the voltage of the plant can be observed. These peak intensities demonstrate
variable responses depending on the time of experimentation (i.e. the associated stage of
senescence). The time-dependent activity associated with responses in voltage to peak intensities
may reflect the necessary alterations in the distribution of potential and free energy within the
system (plant). The potential redistribution of energy may be inherently linked to the function of
light sensitive channels or other cellular processes (Krol and Trebacz, 2000). Identifying distinct
wavelengths of light along the spectrum can be used as an inference of the particular biochemical
changes in the plant.
Synthesizing the results from the factor analysis as well as the discriminant analysis
supports a unifying notion of a residual plant electrodynamic profile that shares discrete sources
of variance across a temporal duration. Classification of different stages of senescence did not
yield successful 100% classification accuracy. The reduced classification accuracy (to 74% of
cases) of electrophysiological variables suggests a common source of variance that remains even
though the plants may be undergoing marked changes in physiology. The notion that a functional
residual activity, one that is shared or which can be elicited during the appropriate application of
varying peaks of light, primarily associated with electrodynamic properties, is corroborated by
the results of our factor analysis. They suggest a shared source of variance between green and
senesced leaves and their associated stages of senescence when presented with varying
intensities of photo-stimulation. These results indicate that even though the senesced leaves had
qualitatively less chlorophyll at the beginning of the experiment, the resultant responses to
different intensities of light remained the same. Responses of the plants to light at different
temporal frames, which may be correlates of different stages of senescence, demonstrate a
similar trend with respect to electrophysiological changes.
36
Furthermore, the lack of discriminative capabilities between the photo-stimulus and the
photo-absent periods (data not presented) of this paradigm suggests that regardless of the degree
of chlorophyllic degradation, the activity of both the senesced leaves and green leaves share an
underlying mechanism. An alternative hypothesis suggests that there seems to be a conservation
in activity of the electrodynamic responses of leaves over a discrete period of time, regardless of
their integrity, to some exogenous stimuli. The residual activity present in plants undergoing
senescence reveals a hidden characteristic suggesting that plants that have marked degradation in
chlorophyll composition still undergo responses to light. We do not suggest that plants
demonstrate an alternative means of photosynthesis. We do suggest however that plants possess
an alternative means of collecting information, in the form of light, which may be contributing to
observed changes in electrical potential.
An additional hypothesis as to the mechanism by which light may be interacting with the
plant in a significant manner with respect to changes in electrical potential is through the
interaction of phytochromes. Newman and Briggs (1971) demonstrated that potential differences
of varying intensities could be elicited when plants were exposed to incident light within the red
and far red spectrums and whose action was mediated through phytochrome activity.
Furthermore, Yanovsky et al. (1997) and Kircher et al. (2012) observed differential responses in
the phytochrome subunits when exposed to low intensities of red and far-red incident light. We
suggest this as a possible mechanism of interaction as the applied light intensity is remarkably
smaller than daylight or the intensity of light required for photosynthesis. Arguments for the
latter mechanism are supported by the spectral power distribution of incident light whose peaks
were in the red and far red spectrum (Osram Sylvania, 2000).
37
2.5.2 Temporal Variation of Senescence Onset
It is suggested that the time-course of this effective change in electrophysiology is
associated with the 3rd week of measurement. However, we must be particularly vigilant on the
identification of this temporal phenomenon as the change in the local environmental variables
(change in season) peaked around this time. One particular argument in support of an actual
change in physiology associated with the gradual change in the environment is supported by the
results obtained from our discriminant analysis. The homogeneity of values obtained from the
discriminant function for Weeks 2 and 3 are in good approximation of each other. Taking into
consideration both the results obtained from the multiple regression and discriminant analyses,
the likely temporal evolution of senescence demonstrates a gradual alteration in the
electrophysiological parameters of the plant which peaks around Week 3 and whose changes are
evident in Week 4 measures with respect to this particular experiment.
2.5.3 Quantitative Support for Electrophysiological Alterations
We have suggested in this document that chlorophyll is not necessarily responsible for
the onset of senescence nor does it have to directly contribute to the underlying changes in
electrophysiology of the plant. It is possible to demonstrate measurable changes in electrical
potential of plants even though their chlorophyllic content has been compromised. We have
suggested that perhaps other biological agents such as structural or enzymatic proteins may be
responsible for the generation of voltage-associated changes in whole plant physiology.
However, an alternative hypothesis suggests that the observed changes in electrical potential may
arise from physical sources related to, or generated by, changes in the inherent electric and
magnetic properties of the plant in response to light (electromagnetic radiation).
38
The electric field can be calculated by dividing the voltage by a distance. Here, if we
assume that the change in voltage is in the order of millivolts over a linear distance of 20 cm (the
distance between measuring sensors), the resultant electric field would be in the order of 10-
2V∙m-1. Using Maxwell's equation for the scalar field component ( ) where ρ is the
charge density, is the permittivity of free space, E is the electric field and is a vector, and
assuming that the magnitude of the scalar component is maximized ( = 1) and radiates
isotropically, we can re-arrange the equation to find the charge density (ρ) which would yield a
solution whose value would approach 8.85∙10-14 As∙m-2. If we multiply the charge density
8.85∙10-14 As∙m-2 by a frequency (s-1), we would get a current density J (A∙m-2). In this case we
will assume that 10 Hz as a fundamental frequency for plant cell activity, derived from the range
of conduction velocities (a mean between 1 and 100 Hz) from Masi et al. (2009) and Fell and
Zimmerman (2007), the resultant value would be in the order of 8.85∙10-13A∙m-2.
Now, if we take Maxwell's magnetic field equation ) and substitute the
values of current density (J = 8.85∙10-13A∙m-2), µ (1.26∙10-6 N∙A-2) the permeability of free space,
ε (8.85∙10-12 C2∙N-1m-2) the permittivity of free space and the value of 1.0∙10-2V/m for E. Over a
period of 30 sec and would approach 1.12∙10-18 kg∙A-1s-2, where 30 sec was chosen based off of
conduction velocity parameters ranges (seconds to minutes) and reflects a mean value for this
distribution. Furthermore, the onset of voltage changes, as measured in this experiment,
demonstrate a 30 sec delay of onset before these changes were qualitatively different in raw
recordings. We assume, as well, that the maximum intensities derived from this calculation is
approximated when the operator, , is set to unity (1). We also consider that due to the changing
nature of these fields these maxima occur transiently but contribute to the dynamic nature of
observed plant potentials.
39
If we multiply the intensity of the magnetic field by the product of the magnetic
diffusivity and the current density we would yield a kg∙s-3. Here the magnetic diffusivity is the
result of 1/µσ, where µ = 1.26∙10-6 N∙A-2 and the conductivity (σ) is 5.0∙10-3 kg−1∙m−3∙s3∙A2 for
plants (Simpson and TenWolde, 1999), ranging between 1.08∙10-5 to 1.14∙10-2 kg−1∙m−3∙s3∙A2
depending on the tissue and plant type (Stiles and Jörgensen, 1914), resulting in a value of
1.59∙108m2s-1. The product of the current density (8.85∙10-13A∙m-2) and the magnetic diffusivity
(1.59∙108m2s-1) results in a value of 1.41∙10-4As-1. If we multiply this value by the magnetic field
strength 1.12∙10-18 kg∙A-1s-2 we would get a value of 1.58∙10-22kg∙s-3. Assuming the lower limit of
time necessary for a plant to demonstrate a change in voltage, which would approximate a
duration in the order of a second (1 second) as derived from conduction velocities, the result
would be 1.58∙10-22J∙m-2. If we divide the energy density by 2.0∙10-20J we would get a value of
1.274∙102m2. Given that plant cells have a surface area of 1.0∙10-8m2, the total number of cells
that would be affected by this millivolt equivalent depolarization would be in the order of 1010.
Now, if we examine the static electric field component where of a given
plant operating at -300 mV to 500 mV (daily observations) over 20 cm, the resultant electric
field strength would be 1.5 to 2.5 kg∙A-1s-3, assuming that is set to 1. If we take the charge
density of 8.85∙10-14 As∙m-2 and multiply by 1.59∙108m2s-1 we would produce a current of 1.41
∙10-5A. The product of the changing magnetic field 1.5 kg∙A-1s-3 and the current 1.41∙10-5A
results in a value of 2.12∙10-5 Wm-2. Assuming again that this change occurs over a period of 1
second, defined by the lower limit of the time component for the onset of voltage changes based
on conduction velocities, the value would equal 2.12∙10-5 Jm-2, where dividing this value by the
fundamental quantum value of 10-20J gives you a value of 10-15 m2, whose equivalent linear
distance would be in the order of 3.16∙10-8m, or the approximated value of the thickness of the
40
cell membrane (Hine, 1989). Regardless of the change in the magnitude of the static potential
difference, be it either ± 300 to ± 500 mV (daily observations), the only change would be found
in the coefficients and not the magnitude of the relationship. It should be noted that the polarity
of the static electric field component only changes the direction of the magnetic field.
We can further relate the electric field strength to a change in the polarity along this axis.
Consequently the change in energy can be calculated as the product of the change in potential
and the fundamental unit charge (1.6∙10-19 As), for which a millivolt change is equivalent to
1.6∙10-22 J. What is interesting however is that there is usually a change in polarity (dipole
moment) with the application of light (data presented as daily observations). Here the change in
the dipole moment (Ams) can be calculated as the change in energy divided by the change in
electric field strength (J / V∙m-1). The change in dipole moment derived from a change in energy
of 1.6∙10-22 J and a change in electric field strength of 1.0∙10-2 V∙m-1 is in the order of 1.6 ∙10-
20Ams. The equivalence of a change of dipole moment whose magnitude is 10-20 Ams is
essentially the distribution of one charge over the distance between sensors. The change in dipole
moment, equivalent to a single charge along the 20 cm separation between sensors, may suggest
one of two things: 1) a change in dipole moment equivalent to the movement of a single charge
may be considered as the same as the movement of electrons traveling through a conducting
material at drift velocity, and 2) the change in dipole moment may reflect a threshold of a
number of cells that are required to elicit a generalized response. In order to examine the nature
of the latter idea we investigate the relative change in magnetic moment that accompanies our
millivolt depolarization.
The magnetic moment is related to the change in electric field strength by resistivity and
conductivity, the latter of which is the reciprocal of the former. To calculate resistivity, and
41
consequently the conductivity, we must first calculate the net charge per area. If we take the
threshold of stimulation to be 25 lux or the equivalent of 3.75∙10-2 W∙m-2 and the net change in
potential difference to be 1.0∙10-3V, the reciprocal of the charge per area would be the quotient
of the voltage and the power density whose resultant value is in the order of 2.67∙10-2 m2∙A-1. To
calculate the resistivity we would simply multiply the electric field strength (1.0∙10-2 V∙m-1) and
the reciprocal of the charge per area 2.67∙10-2 m2∙A-1 whose value would approach 2.67 ∙10-4Ω∙m.
This resistivity, or its reciprocal conductivity, can be inserted in to the equation for magnetic
diffusivity which is defined as η = 1/(σ∙µ), where η is the magnetic diffusivity, µ is the
permeability of free space (1.256∙10-6 N∙A-2) and σ is the conductivity (3.75∙103 A2s3∙kg-1m-2).
The resultant magnetic diffusivity would be 2.126∙102m2s-1. The magnetic moment can then be
calculated through the operation of multiplying the unit charge (1.602∙10-19As) and the magnetic
diffusivity (2.126∙102m2s-1) which approaches 3.40∙10-17 A∙m2. We can calculate the total
number of charges associated with the calculated change in magnetic moment by dividing the
calculated change in magnetic moment by the magnetic moment of an electron (9.28476∙10-24
A∙m2). The total number of charges that can be affected by this change in magnetic moment
would be approximately 3.67∙106 charges, which converge on the number of charges required to
generate the resting membrane potential in cells (Persinger, 2014). This may lend credence to the
idea that the entire system is reflected in the sum of its parts and that a single component can
affect the whole (Mach, 1887, Persinger, 2012).
From our results, it is suggested that there exists a finite limit of incident radiation which
can preferentially alter the electrodynamic properties of sedge plants undergoing senescence in
response to light. Here we describe the finite limit for the elicitation of voltage changes to be in
the range of ~20 lux. A potential explanation for this limit is explored as the necessary threshold
42
to attain quantum-cell equivalence. The incident radiation of ~20 lux would have a
corresponding power density in the order of 3.0∙10-2 W/m2. In order to convert a power density to
an energy density we would simply have to multiply by a time, or conversely divide by a
frequency. Assuming that temporal component of the emitted light is 3.0 ∙1012 Hz (the frequency
equivalent of 2.0∙10-20J) the energy density would be in the order of 10-10J/m2. If we use E = mc2,
where E is the energy, c is the speed of light and m is the rest mass of the photon (10-52kg), the
energy equivalent would be in the order of 10-36J and we would be able to extract the number of
photon equivalents of this process. The resultant quotient of the energy density of 10-10J/m2 and
the energy of a rest mass photon (10-36J) would be in order 1026 photons/m2. Assuming that the
photon equivalent of 10-20J is roughly 1016 photons, then the number of quantum units that can
be affected by 20 lux is in the order of 1010 quanta/m2. If we were to apply this energy over the
surface area of a cell (10-10m2) we would end up getting 1 quantum/cell. Alternatively, the
energy equivalent or information equivalent, if applied over 1 m2, would be enough to activate
1010 cells. This may represent the threshold necessary to re-activate plant photosystems or be the
required energy to depolarize the cell and initiate the necessary physiological responses. It is
suggested that anything below this threshold would not generate the necessary quanta-cell
equivalence.
To further our exploration of the convergence of effects found in the electrophysiological
properties of plants let us examine the capacitance associated with light stimulation. Capacitance
can be calculated as the quotient of a unit charge divided by a voltage. Given that the unit charge
is 1.602∙10-19 As and the change in potential difference is 1.0∙10-3 kg∙m2∙A-1∙s-3 the resultant
capacitance would be in the order of 1.602∙10-16 A2∙s4∙kg-1∙m-2. If we postulate that the integrity
of the resting membrane potential is maintained by 106 ions then the total capacitance would be
43
8.01∙10-10 A2∙s4∙kg-1∙m-2. Capacitance is related to energy by the relationship of E = ½ CV2 where
E is the energy in Joules, C is the capacitance and V is the voltage. The resultant energy would
be 8.01∙10-17 J given that the change in voltage is 1.0 mV and the capacitance is 8.01∙10-17
A2∙s4∙kg-1∙m-2.
Using the energy calculated in the previous section we can approximate a given
wavelength equivalent of a stimulus using the relationship of E = h∙c/λ where h is Planck's
constant (6.62 ∙10-34 kg∙m2∙s-1), c is the speed of light (3.0∙108m∙s-1) and λ is the wavelength in
meters. Solving for this equation using an energy of 8.01∙10-17J provides a wavelength equivalent
of 2.50∙10-9m. The value of 2.50∙10-9m is the equivalent length of approximately 10 water and 10
hydronium ions, converging on the total possible number of water molecules that can be present
in an aquaporin channel at a given time (Agre, 2006). The electrophysiological properties of
whole plant senescence reflect the microstructural alterations at the level of the cell membrane
and the dynamic alteration of the osmotic characteristics at this level. Furthermore, we postulate
a cell-quantum equivalent of 1 photon that may be necessary to re-initiate cellular activity and
displace the plant's senesced state.
44
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Bleecker AB, Patterson SE. Last exit: senescence, abscission, and meristem arrest in
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conserved and novel mechanisms. Annu. Rev. Plant Biol., 57, 675-709.
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autumn and early winter. Plant Ecology & Diversity, 2(1), 27-35.
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investigation in plant physiology. New Phytologist, 13(6‐7), 226-242.
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47
Chapter 3
3 Experimental Trans-Species Excess Correlation
3.1 Abstract
Experimental evidence of entanglement and entanglement-like phenomena has been increasing
steadily in the last decade. One example of entanglement phenomena is referred to as excess
correlation (XSC), where there is a relative increase in the strength of a relationship between two
spatially isolated locations. The Dotta-Persinger method for eliciting XSC employs identical
electromagnetic field (EMF) applications at both local and non-local sites to homogenize the
spaces, and timed injections of hydrogen peroxide at the local site. We observed the responses in
electrical potential from two different species – Glycine max and Escherichia coli – with each
species alternatively acting as both local and non-local sources. Using EMFs with 3 msec point
durations led to an enhancement of the correlation strength compared to controls, but reversed
the direction of the correlations. 1 msec point durations also enhanced the correlation strengths
but without the directional change. The strength of the correlation also differed dependent on
which species received the peroxide injections (local site). Taken together, these results suggest
that XSC phenomena can occur between two different species, and is further confirmation that
the application of identical electromagnetic fields at two spatially isolated spaces essentially
unifies the locations.
3.2 Introduction
The superposition of space-time frames upon separate systems can result in the generation
of spooky events that are not necessarily reflective of the causal nature of temporal relationships.
48
This "spooky action at a distance” was first coined by Einstein, and is expressed as the
phenomenon of entanglement (Einstein et al., 1935). The nature of this effect can be likened to
the simultaneous application of a stimulus at two locations, but where only one location actually
receives any stimuli. However, the spatial separation between sites implies that the spaces be
treated as a unitary location (same space) (Persinger and Dotta, 2011). Consequently, a change in
the behaviour of one site results in a net change of opposite polarity and exact magnitude in the
other site (Hoffman et al., 2012; Hill and Wooters, 1997). Under the appropriate application of
rotating magnetic fields, it is possible to demonstrate a simultaneous doubling in photon
emission from spatially separated chemical reactions (Dotta and Persinger, 2012). The result of
increased spatio-temporal coherence, as measured by changes in the degree of photon emission,
can be inferred as a measure of the degree of relatedness between systems. This entanglement-
like process has been coined “excess correlation", the term arising from the relative increase in
the strength of the relationship between spatially isolated sites (Bennet and DiVincenzio, 2000).
To date, several experiments have studied the operational extent of excess correlation,
through examining changes in the amount of photon emissions (Dotta and Persinger, 2012), the
degree of shared alterations in pH measures (Dotta et al., 2013; Rouleau and Carniello, 2014)
and shared quantitative measures of the brain's electrical activity (Dotta et al. 2009; Dotta et al.,
2011; Burke et al. 2011). Corroborated findings from all of these experiments determined an
effective temporal window for maximizing the effects of excess correlation. Tandem exposure to
effective bulk decelerating, immediately followed by accelerating, electromagnetic field
configurations demonstrated an enhancement in the correlation between spatially isolated
systems. The effective temporal window for maximizing the excess correlation, denoted as the
enhancement of correlation strengths between spatially isolated systems, occurs between 7 and 8
49
min after the initiation of the accelerating (second) field. Thus, the effective temporal dispersion
between 7 and 8 min will be examined extensively in this experiment.
However, these experiments have been predicated on the structural homology of the
separated systems. A current knowledge limitation persists regarding the potential applications of
excess correlation, as the functional extent is entirely based upon the shared geometry of the
interacting centers. That is, one can only influence X-matter when X-matter is present at both
local and non-local centers, where the terms local and non-local refer to areas receiving direct
stimulation (local) or no stimulation (non-local). Ergo, the presupposition of entanglement-like
phenomena relies on substance or spatial organization.
Structure - at least the aspect of ordered, discrete units - is strictly related to the occupancy
of matter. Matter results in substance, a tangible construction of geometries, and a form of
structure resulting in spatial organization. Conversely, structure can exhibit time-like qualities
that do not change drastically from one measure to the next resulting in a process. Processes
form a temporal pseudo-structure that is the transient organization of underlying activity.
Transient organization of any process creates a fluid order that does not necessarily, directly or
indirectly, result in a change in the overall spatial structure of the substance. Thus temporal
structure can be defined as a process by which a measurable change can be elicited in a material
without the decomposition of the original spatial organization.
If a requirement of excess correlation is structure, then temporal structure common to both
distinct spatial organizations (material, species, etc.) should be an equal candidate for this type of
investigation. The purpose of this experiment was to examine whether differences in structure
can still appreciably demonstrate excess correlation between spatially separated systems. We
50
have utilized two unique species (plants and bacteria) in order to investigate whether structure is
a requirement for excess correlation.
For our application the most probable process to be considered as a candidate for the
application and generation of temporal homogeneity is the reaction of plants and bacteria to
hydrogen peroxide. Hydrogen peroxide undergoes a radical cleavage to form the radical version
of hydroxide ions (Inoue and Kawanishi, 1987; Floyd and Lewis, 1983). The increase in
available free radicals can accrue large amounts of damage to DNA, mitochondria, cellular
pathways and cell integrity (Yakes, and Van Houten, 1997; Richter et al., 1988). Furthermore,
sufficient DNA damage can elicit SOS mechanisms and ultimately, given the appropriate
concentrations, may result in apoptosis and/or necrosis (Slater et al., 1996; Gallucci and
Matzinger, 2001). These processes are common to both plants and bacteria (Levine et al., 1994;
Hassett et al., 1987) resulting in a homogenous source of variance. The homogeneity of
processes between these two species should allow for the transient elicitation of the excess
correlation effect.
3.3 Methods
3.3.1 Specimen:
Bacterial cultures of Escherichia coli (11303) were obtained from the American Type
Culture Collection (ATCC), Manassas, VA, USA. Bacteria were sub-cultured from a stock plate
and inoculated in 10 mL of nutrient broth media (Oxoid CM00001) for 24 h at 37⁰C to a final
cellular concentration of 108 to 109 cells/mL. Plant species consisted of Glycine max (soybean)
seeds that were imbibed on moistened filter paper for approximately 3-5 d before testing. All
seedlings were subjected to 14 f of continuous light stimulation and provided with 1-2 mL of
51
water per day. All plant specimens were purchased from Stokes Seeds Canada and were not
genetically modified.
3.3.2 Measurement and Sensor Placement:
Measurements of DC (direct current) potential differences were simultaneously recorded
from both G. max samples and E. coli cultures. DC potential difference was recorded by two
separate digital multimeter units (Victor 70C) that sampled once every 5 sec with an uncertainty
of ± 0.1 mV. Data from the mulimeters were recorded with two laptops: a Lenovo ThinkPad
Y580 and a Samsung Notebook model NP550P5C. The combination of computers and their
associated multimeters did not differ between trials. The individual multimeters possess internal
time-keeping functions that are synchronized and any delays subject to the individual port times
of the laptops are negligible.
Sensor placement for plant specimens consisted of small circuit connectors implanted
directly into the seedlings. The free ends of the circuit connectors were connected to alligator
clips on the anode and cathode of the multimeter. The implanted sensors were placed at the distal
end (apex) of the seedling and was considered the reference (negative) lead. The measuring
sensor (positive lead) was implanted into the initial germinative site of the root (the root
protrusion). The seedlings were suspended in 6 mL of distilled water and placed in a 10 cm cell
culture dish. A volume of 6 mL of distilled water was selected as the minimum required volume
to accommodate complete coverage of the bottom of the culture dish.
Bacterial broth cultures (10 mL nutrient broth) were similarly suspended in 10 cm culture
dishes where bacterial sensors were constructed by stripping approximately 2.5 cm off of the
ends of 10 gauge insulated electrical wire. One end of electrical wire was submerged into the
culture dish whilst the other end was attached to the positive and negative leads of the digital
52
multimeter. The separation between the two sensors in the culture solution was approximately 2
cm. Composition of the insulated wire was non-braided (solid), double stranded copper.
3.3.3 Elicitation of Excess Correlation:
Both G. max and E.coli were subjected to a specific application geometry of angular
accelerating or decelerating fields with fixed point durations. The selected point durations were 3
and 1 msec points in accordance with previous research (Rouleau et al., 2014). The modulation
of the angular component was accommodated by changing the duration of the inter-stimulus
delay. Decelerating angular velocity was modulated by serially increasing the duration of the
inter-stimulus delay by 2 msec from an initial duration of 20 msec to a final duration of 32 msec.
Conversely, the accommodation of accelerating angular velocity was produced by serially
decreasing the inter-stimulus delay by 2 msec from an initial duration of 20 msec to a final inter-
stimulus delay of 8 msec. Both accelerating and decelerating angular velocities were
continuously looped for their entire exposure duration (Rouleau et al., 2014). A visual
representation of the order of the fields, their associated point durations and the constructs of the
local and non-local sources are presented in Table 3.1.
53
Table 3.1: The identification of the experimental parameters with appropriate point durations and
accelerating/decelerating rotating magnetic field configurations.
Local Species Orientation Point Duration
(msec)
Field 1 Field 2
E.coli (bacteria)
SHAM
------------------- --------------------
G. max (plant) ------------------- --------------------
E.coli (bacteria)
Forward
1 and 3 Decelerating
Angular Velocity
Accelerating
Angular Velocity G. max (plant) 1 and 3
E.coli (bacteria)
Reversal
1 and 3 Accelerating
Angular Velocity
Decelerating
Angular Velocity G. max (plant) 1 and 3
An Arduino Uno microcontroller was utilized for the generation of angular accelerating
and decelerating fields. The microcontroller altered the voltage supplied through two toroids,
constructed using 14 gauge single stranded insulated stereo wire, to produce the electromagnetic
field. The wire was wrapped 216 times around a plastic 10 inch crochet hoop purchased from a
local crafts store. The Arduino microcontroller was attached to a circuit board which interfaced
with the toroids and all field parameters were written in the associated Arduino programming
software. Presence of field applications were verified using an AC milliGauss meter (model
UHS, AlphaLabs, Inc USA) measuring tri-axial fluctuations in applied field intensities. When
either the accelerating or decelerating angular velocity fields were presented there was a shift in
background intensities of 3 nT. Specimens were placed in the center of the toroid in all
conditions. A visual representation of the experimental set-up is presented in Figure 3.1.
All specimens were exposed to three conditions: Sham, Forward, and Reversal. Sham
conditions consisted of the experimental paradigm being run as outlined, with the exception that
the equipment was kept off for both specimens. Forward field presentations consisted of 6 min of
54
the decelerating angular velocity field followed immediately by 12 min of the accelerating
angular velocity field. Conversely, the reverse field presentations consisted of 6 min of an
accelerating angular velocity field followed immediately by 12 min of the decelerating angular
field. Furthermore, 2 min pre-field exposure measures were also taken in all application
geometries for a total experiment duration of 20 min. Sham, Forward and Reversed field
presentations were presented for both 3 and 1 msec point durations which were selected as they
overlap with calculated values associated with proton and electron dynamics (Persinger and
Koren, 2007).
Figure 3.1: A diagrammatic representation of the experimental apparatus. Abbreviations are as follows DMM:
digital multimeter, M.C: microcontroller.
Local Non-Local
Injection
55
In accordance with past evaluations of the excess correlation phenomenon, the
introduction of a process was utilized in order to demonstrate dynamic alterations in DC profiles
of the separate species. Here the process was the direct injection of 50 μL of 3% hydrogen
peroxide purchased from a local pharmacy. Injections occurred at min 4, 7 and every min until
min 15 into the initiation of the applied fields. Selection of hydrogen peroxide was based on the
shared reactive oxygen species (ROS) response mechanisms of the distinct species and the
notion of electron movement associated with the classical mechanics of electrical potential
difference. The injection site, which consisted of one species, was considered the local site whilst
the site and species that did not receive a direct injection of 3% hydrogen peroxide was
considered the non-local site. In this design both plant and bacterial species served as alternating
local and non-local sites. Rotations of the local and non-local species were conducted in order to
identify the directionality of the efficacy of the elicitation of excess correlation.
3.3.4 Statistical Methods:
All data was collected using the software associated with the digital multimeter
measuring devices and exported into SPSS v.17 statistical software for analysis. Data was
subjected to a series of linear and non-linear curve fitting equations of which the cubic regression
demonstrated the best fit for all cases (adj. R2 =.146 X 1.00, p<.05). From these analyses all
data were cubically detrended to reduce the variability associated with time and the residuals
were saved. Subsequent to detrending, residuals were standardized before further analyses were
conducted. The resultant standardized residuals were used to compute Stouffer's composite Z-
scores using Equation 3.1 to assess overall group effects between point durations and field
presentation parameters over successive trials. That is to say the composite Z-score was
56
computed in order to determine net field effects irrespective of the potential influences of the
local species.
Equation 3.1: Zc = /
Where Zc is the cumulative/composite Z-score, Z is the Z-score for the individual trial and n is the number of trials.
Data were subdivided into four specific time bins (with their duration in brackets) and are
identified as baseline (2 min), Field 1 (6 min after initiation of the first field presentation), Field
2A (the first 8 min of the second field) and Field 2B (the last 4 min of the second field).
Construction of these temporal windows was used to identify specific instances in the excess
correlation design where the effects were generally demonstrable. Partial correlation coefficients
between local and non-local sites controlling for time were employed to examine the effects of
the field presentations. Furthermore, standard error measures for each of the specific time bins
were estimated for all composite scores by applying Wquation 3.2.
Equation 3.2:
Where r is the correlation coefficient and n is the number of points in the distribution being assessed.
3.3.5 Geomagnetic Data Extraction:
The weak intensities of the applied fields in these experiments show marked overlap with
background fluctuations in the strength of the geomagnetic field. Thus, we examined the
potential synergistic nature of the combination of planetary geomagnetic perturbations on local
applied magnetic fields. Data for planetary magnetic field measures were extracted from the
Space Weather Prediction Center's online database
(ftp://ftp.swpc.noaa.gov/pub/indices/old_indices/) for the day of testing as well as three days
before and after a particular experiment. All data were entered into SPSS for further parametric
57
and non-parametric testing. Data for the magnitude of the correlation coefficient for the first 8
min of the second field were computed for each individual trial.
3.4 Results:
3.4.1 Electrophysiology of Excess Correlation:
An example of the raw electrical potential dynamics between bacteria and plants has been
presented in Figure 3.2. This demonstrates a conspicuous association between the changes in
electrical potential of both the local and non-local sites. Examining the non-specific effects of
the applied magnetic fields on the inter-relatedness between local and non-local sites reveals an
overall effective trend associated with the combination of 3 msec point durations in the forward
geometry (z= 12.51, p <.001, Figure 3.3). All other values demonstrated significantly (p<.05)
weaker partial correlation coefficients when compared to controls.
Figure 3.2: An example of the raw electrophysiological profile of local and non-local interactions. Downward
arrows demonstrate when injections were made, and upward facing arrows denote when the fields were applied.
58
Figure 3.3: The non-specific effects of the applied magnetic field conditions on the degree of correlation between
local and non-local sites. Error bars represent calculated standard error measures.
Further analysis of group effects were conducted in order to effectively assess the
influence of local species type on the efficacy for eliciting excess correlation. The resultant
Stouffer's composite Z-scores produced values for the orientation of the local and non-local
species. Pearson correlation coefficients were computed for local and non-local sites during
baseline, Field 1, Field 2A and Field 2B conditions. All resulting correlation coefficients were
used to estimate the standard error of the relationship during the predefined temporal windows
using Equation 3.2.
In order to demonstrate excess correlation effects between species, comparisons between
the different fields of matched local species were conducted; i.e. control cases where bacteria had
received an injection in the absence of magnetic fields were compared to cases where bacteria
had received an injection of hydrogen peroxide in the presence of fields. A comparison between
bacteria as the local source (i.e. receiving a series of injections of 3% hydrogen peroxide)
subjected to the application of 3 and 1 msec point durations with the forward and reverse field
59
geometries is presented in Figure 3.4. Similarly, the comparisons between plants as the local
source and the direction of field application (forward and reversed) and point durations (1 and 3
msec) are presented in Figure 3.5. These were the only conditions which demonstrated a
conspicuous excess correlation-type phenomenon.
Figure 3.4: The nature of the effects on bacteria as a local source is demonstrated in the left (A) and right (B) graphs.
The general trends represent the application of the different field geometries for both 1 (A) and 3 (B) msec
respectively. Error bars represent the estimated standard error of the correlation.
60
Figure 3.5: : The nature of the effects on PLANTS as a local source is demonstrated in the left (A) and right (B)
graphs. The general trends represent the application of the different field geometries for both 1 (A) and 3 (B) msec
respectively. Error bars represent the estimated standard error of the correlation.
Comparisons for the first 8 min of the second field application were conducted between
each of the different combinations of species, point durations and field application order.
Significant differences in the strength of correlations between controls and field exposed groups
were assessed using Fisher's r-to-z transformation (Equation 3.3) and subsequent Z-score
comparisons. For simplicity all statistical results are presented in Table 3.2.
Equation 3.3: Z = 0.5∙ln(1+r/1-r)
Where Z is the Fisher's Z-score value and r is the correlation coefficient you are converting.
61
Table 3.2: Presentation of the differences in the magnitude of the correlation coefficients as a function of species,
field presentation and point duration. All values presented here are compared to controls of the same species during
the first 8 minutes of the second field. The sign of the z statistic denotes the direction of the interaction as the
computation performed was as such that the value of the field exposed groups were subtracted from the values
obtained in sham conditions.
Local Species Field
Presentation
Point Duration Pearson r Z statistic p
E. coli (bacteria)
Excess
Correlation
3 msec
1 msec
-.835
.949
-15.05
5.69
<.001
<.001
G. max (plant) 3 msec
1msec
.784
-.912
-16
-1.79
<.001
.074
E. coli (bacteria)
Reversal /
Inverted
3 msec
1 msec
-.387
.474
-9.6
-3.37
<.001
<.01
G. max (plant) 3 msec
1 msec
.553
-.024
4.49
8.6
<.001
<.001
3.4.2 Relationship between geomagnetic fluctuations and Excess Correlation:
Results of parametric and non-parametric correlational analyses demonstrated no
relationship between the magnitude of the correlation coefficient for either excess correlation or
reversed field presentations. This null relationship remained even when one accommodated for
point duration of effective and non-effective fields. However, strong positive relationships
between sham conditions and geomagnetic activity 2 (r =.875, p<.05, rho =.829, p<.05) and 3 (r
=.938, p<.01, rho =.899, p<.05) days before testing were identified. This may suggest the
possibility of excess correlation like results (i.e. strong relationships) on days where there was no
field presented.
62
3.5 Discussion:
Of primary concern is the heightened strength of the association with respect to the control
conditions. One hypothesis posits that the effects demonstrated may be the result of a unique
resonant feature with geomagnetic fluctuations. Our analyses support this claim by
demonstrating strong associations between the magnitude of parametric and nonparametric
correlation coefficients of control conditions and planetary geomagnetic activity 2 and 3 days
before testing. However, while examining the effective relationship between planetary
geomagnetic activity and days in which fields were applied no significant correlations were
found, similar to previous results with respect to psi and haunt phenomenon (Krippner, 1996;
Persinger, 1985; Persigner et al., 2002; Roll and Persinger, 2001). This implies the possibility
that the geomagnetic field proper may in fact contribute to spurious excess correlation events that
cannot be accounted for given the temporal latency between heightened geomagnetic activity and
the observation a given event.
We have successfully demonstrated that under the appropriate conditions the modulation
of the electrodynamic properties (i.e. the dynamic alteration in potential difference as a function
of time) of a given species can affect another species even though they are separated in both
space and phylogenetic progression. Visual inspection of the graphs reveals a general trend
associated with the effective 3 and 1 msec point durations for both plant and bacteria.
Specifically, the 1 msec point durations fields resulted in an enhancement in magnitude of the
correlation coefficient without a reversal in direction whereas the 3 msec point durations resulted
in a magnitude enhancement and a reversal in direction. These effects are consistent with
previous research using both similar (Rouleau and Carniello, 2014) and different (Dotta et al.,
63
2013) equipment with identical point durations and identical bulk acceleration and deceleration
parameters.
Although the application of the reversed field parameters produced a net change in both the
magnitude and direction of the correlation coefficients, the effects produced by the application of
the appropriate excess correlation fields produced even greater effects. The net result suggests a
gradient of excess correlation when specimens are presented with bulk angular accelerating and
decelerating fields. These effects were not demonstrable using a different apparatus producing
complex accelerating and decelerating components of bulk and phase velocities (Dotta and
Persinger, 2012). These findings may suggest a unique property associated with bulk angular
velocities that are not present when phase velocities are included.
Utilizing Table 3.2 we can identify the strongest coupling between local and non-local
species. The results are suggestive that bacteria acting as the local mediator for energy transfer
between the spatially separated sites is most effective for 1 msec point durations. Although the
effects can be accommodated by both species, this slight skew towards a unique source is
intriguing. It suggests, at least from the perspective of the working model, that there might be
increased coherence between the bacteria (E. coli) and the properties of the applied
electromagnetic field which might reflect interactions at the level of the electron (Persigner,
2013; Persinger and Koren, 2007).
Consider the resting membrane potential of E. coli to be in the order of -140 to -240 mV
(Bot and Prodan, 2010). If a single charge is responsible for the generation and maintenance of
this potential difference then the net energy can be calculated. If we take the product of the upper
limit of the resting membrane potential (1.40∙10-1 kg∙m2∙A-1∙s-3) and the unit charge (1.6∙10-19A∙s)
the resultant energy would be in the order of 2.24∙10-20 J (kg∙m2∙s-2). This value converges on the
64
energy associated with the action potential and that energy required to maintain the separation
between charges along the membrane of animal cells (Persinger, 2010).
If the linear dimension of the E. coli cell is approximately 1 μm (1.0∙10-6 m) (Nanninga
and Woldrich, 1985) then the volume a single E. coli cell occupies would be 1.0∙10-18m3. Given
that the concentration of E. coli is approximately 108 to 109 cells/mL and our solution had a final
volume of 10 mL, the total number of cells would be in the range of 109 to 1010. The net
volumetric occupancy of all the cells would then be approximately 10-8 to 10-9 m3. Taking the net
energy associated with a single cell (2.24∙10-20 J) and dividing this value by the total volume
occupied by the cells (10-8m3) yields an energy density of 2.24∙10-12 J∙m-3(kg∙m-1∙s-2). It is
possible to calculate the energy density resulting in the application of a magnetic field using
Equation 3.4:
Equation 3.4 : J∙m-3 = B2/2µ
where B is the strength of the magnetic field in Tesla (kg∙A-1s-2) and µ is the permeability of free
space (1.256∙10-6 kg∙m∙A-2s-2). Rearranging to isolate for the necessary field strength producing
an energy density equal to 2.24∙10-12 J∙m-3 results in an intensity of 2.23∙10-9 T. This approaches
the value associated with the intensity of the applied electromagnetic fields employed in this
study.
We may further describe phenomena associated with magnetic fields as they affect the
rate of change of their complimentary electric field. This dynamic interaction can be modeled
using Maxwell's equation for Faraday induction represented in equation 3.5:
Equation 3.5: = (J + ε
65
where is the curl operator, B is the strength of the magnetic field, µ is permeability of free
space, ε is the permittivity of free space, δE/δt is the rate of change in the electric field and J is
the charge density.
If we assume that the curl operator represents a unitary value and that the magnitude of J is
negligible the expression reduces itself such that the strength of the field is related to the product
of the permeability, permittivity and the changing electric field. Re-arranging Equation 3.5, we
can solve for the intensity of the electric field. Given that the permeability is 1.256∙10-6 N∙A-2,
the permittivity is 8.85∙10-12 C2∙N-1m-2, the strength of the applied field is 3.0∙10-9T and the
temporal application duration is in the order of milliseconds (10-3 sec) the net magnitude of the
electric field would be 2.7∙105 V∙m-1 (kg∙m∙A-1s-3). Conversely, if we take the magnitude of the
resting membrane potential of E. coli to be in the order of 10-1 V, the diameter of the cell to be 1
μm (10-6m) the resultant electric field would be in the order of 105V∙m-1. This implies a
convergence of the applied magnetic fields on the operational basis of the cell, with respect to
energies and electric field strengths that may demonstrate a functional mechanism associated
with excess correlation.
66
3.6 References :
C. H. Bennett and D. P. DiVincenzo, Nature 404,247(2000).
Bot, C. T., & Prodan, C. (2010). Quantifying the membrane potential during E. coli growth
stages. Biophysical chemistry, 146(2), 133-137.
Burke, R. C., Gauthier, M. Y., Rouleau, N., & Persinger, M. A. (2013). Experimental
demonstration of potential entanglement of brain activity over 300 Km for pairs of
subjects sharing the same circular rotating, angular accelerating Magnetic fields:
verification by s_LORETA, QEEG measurements. Journal of Consciousness Exploration
& Research, 4(1).
Dotta, B. T., Mulligan, B. P., Hunter, M. D., & Persinger, M. A. (2009). Evidence of
macroscopic quantum entanglement during double quantitative electroencephalographic
measurements of friends vs. strangers. NeuroQuantology, 7(4), 548-551.
Dotta, B. T., Buckner, C. A., Lafrenie, R. M., & Persinger, M. A. (2011). Photon emissions from
human brain and cell culture exposed to distally rotating magnetic fields shared by
separate light-stimulated brains and cells. Brain research, 1388, 77-88.
Dotta, B. T., & Persinger, M. A. (2012). “Doubling” of local photon emissions when two
simultaneous, spatially-separated, chemiluminescent reactions share the same magnetic
field configurations. Journal of Biophysical Chemistry, 3(01), 72.
Dotta, B. T., Murugan, N. J., Karbowski, L. M., & Persinger, M. A. (2013). Excessive correlated
shifts in pH within distal solutions sharing phase-uncoupled angular accelerating
magnetic fields: Macro-entanglement and information transfer. Int J Phys Sci, 8, 1783-
1787.
Floyd, R. A., & Lewis, C. A. (1983). Hydroxyl free radical formation from hydrogen peroxide
by ferrous iron-nucleotide complexes. Biochemistry, 22(11), 2645-2649.
Gallucci, S., & Matzinger, P. (2001). Danger signals: SOS to the immune system. Current
opinion in immunology, 13(1), 114-119.
Hassett, D. J., Britigan, B. E., Svendsen, T., Rosen, G. M., & Cohen, M. S. (1987). Bacteria form
intracellular free radicals in response to paraquat and streptonigrin. Demonstration of the
potency of hydroxyl radical. Journal of Biological Chemistry, 262(28), 13404-13408.
Hill, S., & Wootters, W. K. (1997). Entanglement of a pair of quantum bits. Physical review
letters, 78(26), 5022.
67
Hoffmann, J., Krug, M., Ortegel, N., et al. (2012) Heralded Entanglement between Widely
Separated Atoms. Science, 337, 72-75. http://dx.doi.org/10.1126/science.1221856
Inoue, S., & Kawanishi, S. (1987). Hydroxyl radical production and human DNA damage
induced by ferric nitrilotriacetate and hydrogen peroxide. Cancer research, 47(24 Part 1),
6522-6527.
Krippner, S. T. A. N. L. E. Y., & Persinger, M. I. C. H. A. E. L. (1996). Evidence for enhanced
congruence between dreams and distant target material during periods of decreased
geomagnetic activity. Journal of Scientific Exploration, 10(4), 487-493.
Levine, A., Tenhaken, R., Dixon, R., & Lamb, C. (1994). H 2 O 2 from the oxidative burst
orchestrates the plant hypersensitive disease resistance response. Cell, 79(4), 583-593.
Nanninga, N., and Woldringh, C.L. (1985). Cell growth, genome duplication, and cell division.
In Molecular Cytology of Escherichia coli, N. Nanninga, ed. (London: Academic Press),
pp. 259-318. p.261 figure 1
Persinger, M. A. (1985). Geophysical variables and behavior: XXII. The tectonogenic strain
continuum of unusual events. Perceptual and Motor Skills, 60(1), 59-65.
Persinger, M. A., Cook, C. M., & Tiller, S. G. (2002). Enhancement of images of possible
memories of others during exposure to circumcerebral magnetic fields: correlations with
ambient geomagnetic activity. Perceptual and Motor Skills, 95(2), 531-543.
Persinger, M. A., & Koren, S. A. (2007). A theory of neurophysics and quantum neuroscience:
implications for brain function and the limits of consciousness. International Journal of
Neuroscience, 117(2), 157-175.
Persinger, M. A. (2010). 10-20 Joules as a neuromolecular quantum in medicinal chemistry: an
alternative approach to myriad molecular pathways?. Current Medicinal Chemistry,
17(27), 3094-3098.
Persinger, M. A. (2013). Support for Eddington’s Number and his approach to astronomy: recent
developments in the physics and chemistry of the human brain. International Letters of
Chemistry, Physics and Astronomy, 8.
Richter, C., Park, J. W., & Ames, B. N. (1988). Normal oxidative damage to mitochondrial and
nuclear DNA is extensive. Proceedings of the National Academy of Sciences, 85(17),
6465-6467.
Roll, W.G. (2003) Poltergeists amd haunts. In J. Houran & R. Lange (Eds.), Hauntings and
Poltergeists: Multidisciplinary perspectives (pp. 123-163). Jefferson, NC: McFarland &
Company.
68
Rouleau, N., Carniello, T. N., & Persinger, M. A. (2014). Non-local pH shifts and shared
changing angular velocity magnetic fields: Discrete energies and the importance of point
durations. Journal of Biophysical Chemistry, 2014.
Slater A.F.G., Stefan C., Nobel I., Van Den Dobblesteen D.J., Orrenius S. (1996) Intracellular
redox changes during apoptosis. Cell Death Differ. 3,57–62.
Yakes, F. M., & Van Houten, B. (1997). Mitochondrial DNA damage is more extensive and
persists longer than nuclear DNA damage in human cells following oxidative stress.
Proceedings of the National Academy of Sciences, 94(2), 514-519.
69
Chapter 4
4 Passive Excess-Correlation in Water
4.1 Abstract
Experimental examination of effective excess correlation can be demonstrated in a passive
manner. Spatially isolated beakers of spring water were subjected to the application of identically
counter-clockwise rotating magnetic fields exhibiting bulk accelerating and decelerating angular
velocities with 3 and 1 msec point durations. Simultaneous measures of direct current (DC)
voltage demonstrate shared voltage deviations between the isolated systems even when these
systems were not presented with a stimulus. Data suggest that the appropriate application of
electromagnetic fields with specific application geometries remains a contingency of eliciting the
excess correlation phenomenon. Of all the possible combinations of fields presented only two
geometries were identified as being effective in eliciting passive excess correlation in separated
sources of water. It was determined that 3 msec point durations produced the strongest effect
when applied in a decelerating-accelerating pattern whilst similar results were identified using 1
msec points in an accelerating-decelerating pattern. However, similarities between the present
research and the established model evidently diverge when 1 msec stimulus point durations are
utilized. Possible mechanisms involving the expansion of interfacial water are suggested as the
origin of passive excess correlation.
70
4.2 Introduction
The process in which the physical attributes of two separate particles, such as quantum
spin, can be synchronized over vast distances can be attributed to the quantum mechanical nature
of entanglement (Hoffman et al., 2012; Hill and Wootters, 1997). Effectively, entangled particles
operate in a manner such that a change in one of the paired particles results in a proportional, yet
opposite, change in the other. The complementary change that is exhibited by the entangled
particles is generally conceived of as occurring instantaneously. Conceptualizing the effect of
entanglement would be akin to having both particles sharing similar spaces.
The theoretical application of entanglement is often limited to the scope of fundamental
particles such as electrons and protons. However, experimental evidence suggests that under the
appropriate application of time-varying electromagnetic fields macromolecules interact in ways
that mimic an entanglement type phenomenon (Persinger et al., 2007). The application of
accelerating and decelerating bulk angular velocities, in combination with phase modulated
decelerating and accelerating components, has been shown to elicit excess correlation between
spatially separated locations (Dotta and Persigner, 2012; Dotta et al., 2009).
In the Dotta-Persinger studies, excess correlation was inferred through photon emission
spectra resultant from the reaction of hydrogen peroxide and sodium hypochlorite, which showed
a marked correlation (or doubling effect) between 7 and 8 min after the onset of the second field
(Dotta and Persinger, 2012). Similar effects were demonstrated when spatially separated samples
of water were subjected to injections of acetic acid and shared the same configuration of
complex magnetic fields (Dotta et al., 2013). Exposure to the same combination of magnetic
fields demonstrated a marked coherence between cell culture emission spectra (Dotta et al.,
2011) and human quantitative electroencephalographic measures even when the separation
71
between sources was vast (Burke et al., 2013). Spatially separated water samples subjected to
serial injections of 5% acetic acid (a proton donor) that share bulk decelerating and accelerating
magnetic fields generated from different equipment than the Dotta-Persinger studies replicated
and corroborated their findings (Rouleau and Carniello, 2014).
The net effect of this excess correlation phenomenon is a conspicuous alteration in the
relationship between the sources exposed to the fields, which is made apparent by a relative
increase in the strength of correlations between spatially separated systems, however the
direction of the correlation is opposite with respect to each source. That is to say, if source A is
positively correlated with source B, then source B shows a negative correlation with source A.
Thus far, specific parameters for generating the magnetic fields also appear to be contingent
upon eliciting the excess correlation effect. Systematically changing point durations of the field
pattern around 1 and 3 msec leads to a change in correlation coefficients, i.e. the strength of the
excess correlation effect. It is hypothesized that specific point durations are a requirement for
maximizing the effect based on observations in previous studies (Rouleau and Carniello, 2014;
Persinger and Koren, 2007; Persinger, 2014a; Persinger, 2014b).
The selection of 3 and 1 msec point durations was based upon the marked convergence of
the physical properties of the proton and electron, respectively (Persinger and Koren, 2013).
Theoretically, the combination of an external force (a reaction) coupled with the presentation of
bulk accelerating and decelerating angular velocity fields comprised of discrete 3 and 1 msec
point durations are considered the requirements to elicit excess correlation. As such, the limits to
eliciting excess correlation have only been examined with respect to the configurations of the
applied electromagnetic fields. It should be noted that in this design there was no exogenous
application of a process (local injections) which has been a staple of the Dotta-Persinger excess
72
correlation method. The purpose of the present study was to investigate whether two spatially
isolated sites could demonstrate excess correlations in two homeostatic systems; i.e. passively
entangling the locations. For the purposes of this study, “passive correlation” refers specifically
to employing a modified Dotta-Persinger methodology, utilizing the electromagnetic field
applications but not the injections of hydrogen peroxide or acetic acid.
4.3 Methods
4.3.1 Measurement Apparatus:
The experimental design consisted of passively monitoring the direct current (DC)
electrical potential of President’s Choice Brand spring water under the appropriate application
geometry of angular accelerating and decelerating counter-clockwise rotating magnetic fields.
Water was sampled from either a communal 4 L container or individual 500 mL bottles in order
to examine possible differences between the sources. 25 mL samples of room temperature spring
water were placed in open beakers while simultaneous measures of DC potential were recorded.
The potential difference of the water samples was measured using a Victor 70C digital
multimeter. Sampling occurred at 5 sec intervals with an uncertainty of ±0.1 mV. Recorded
values were contingent upon the internal clock mechanism of the digital multimeters and thus
delays associated with the respective computer port times are negligible.
Silver-chloride disc sensors modified from a Grass P79 electroencephalographic (EEG)
recording device were employed to record potential difference. Two sensors were fixed with
electrical tape to separate phallic, carbon-graphite rods, attached to a central rod in order to
maintain a constant distance between disc surfaces. The heights of the sensors were as such that
the entire surface of the discs were submerged below the surface of the water. However, the
sensors were affixed in such a way so as to completely eliminate contact between the suspended
73
carbon-graphite rods and the surface of the liquid (Figure 4.1). The local (L) and non-local (NL)
source beakers had their multimeter data actively recorded on two separate laptop computers, a
Samsung NP550P5C and a Lenovo ThinkPad E531, respectively. The laptop-multimeter
combinations remained the same for the duration of the experiment. The selections of these
terms are arbitrary with respect to the design presented here.
Figure 4.1: Photograph of the sensor configuration and accompanying toroid.
4.3.2 Magnetic Field Configurations:
Water samples were subjected to a combination of angular accelerating or decelerating
counter-clockwise rotating electromagnetic fields with fixed point durations. A point duration is
the length of time in which a current is transmitted through the magnetic field coil from the
microcontroller. The point durations selected for this experiment were 1 and 3 msec which
converge upon theoretical and practical applications. Production of the accelerating or
decelerating angular velocities for the counter-clockwise rotation was accomplished by serially
74
modulating the inter-stimulus delay. The inter-stimulus delay is the amount of time when no
current is being transmitted through the coil. A serial reduction of the inter-stimulus delay by 2
msec from an initial value of 20 msec to a final 8 msec constituted the accelerating angular
velocity field. Conversely, serially increasing the inter-stimulus delay by 2 msec from an initial
value of 20 msec to a final 32 msec constituted the parameters of the decelerating angular
velocity. Electromagnetic fields were continuously generated for the entire duration of their
respective exposures.
The electromagnetic fields were generated using an Arduino Uno microcontroller and
simultaneously presented through two matched toroids that interfaced at a circuit board
generated from the L-laptop (Figure 4.2). Toroids are electromagnetic field coils constructed by
wrapping 14 gauge insulated copper wire around a plastic hoop. Systematic measurements of the
strength of the accelerating and decelerating rotating angular velocity fields were taken using an
AC milliGauss meter (model UHS, AlphaLabs, Inc USA), which showed 0.03 mG oscillations
in electromagnetic field intensities. These 0.03 mG, or 3 nT, perturbations were not present when
the fields were removed nor were they present in sham conditions.
75
Figure 4.2: The representation of the experimental set-up. Here the abbreviations are as follows B1; beaker 1, B2:
beaker 2, M.C; microcontroller, DMM: digital multimeter.
Water samples were placed in the center of the toroid-coils and exposed to either the
sham, forward, or reverse field paradigms. For the magnetic field conditions both 1 and 3 msec
point durations were used in separate trials. The exposure paradigm for both field conditions
begins with a pre-exposure 2-min baseline recording. After the baseline, the first magnetic field
pattern is presented for 6-min, followed by the second field for 12-min. Finally, a post-exposure
baseline measure is recorded for 2-min. The total duration of the paradigm is 22 min (Table 4.2).
Table 4.1: Representation of the field application series and associated point durations
Designation Point duration Field 1 Field 2
Sham
------------------------ ------------------------ ------------------------
Forward
3
1
Decelerating Accelerating
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Reversal
3
1
Accelerating Decelerating
4.3.3 Excess Correlation Selection Criteria:
To determine whether specific application geometries elicited a comparable trend as in
previous research critical criteria had to be met. Here the criteria applied to this phenomenon
are: 1) transition between the application of the first field and the first 8 min of the second field
must result in significant differences in the magnitude of correlation coefficients, and 2) the
magnitude of the correlation coefficients during the application of the second field of a given
geometry of interest must be significantly different from controls. Differences in the strength of
correlation coefficients were computed using Fisher's r-to-z method.
4.3.4 Statistical Methods:
A raw time series data sample before any statistical transformations but demonstrating
the excess correlation effect is provided in Figure 4.3. Subsequent to visual data inspection raw
data for all trials were imported into Microsoft Excel 2007 and IBM SPSS 17 for further
analysis. Due to large discrepancies between measures of electrical potential between trials all
data was within-subject Z-scored to reduce the influence of different initial conditions. Pearson
correlations were computed for all Z-standardized data for min by min analysis. Further
statistical explorations examined relevant time-periods within the excess correlation design and
are specified in Table 4.2 below. Mean standardized Pearson correlation coefficients were
computed between the independent and communal sources of water for analysis between sample
sources. Distribution of data into appropriate temporal bins was constructed in accordance with
77
the divisions of Table 4.2 and is demonstrated in all subsequent and relative graphical
representations and their respective statistical analyses.
Table 4.2: A representation of the time-course of the experiment and the associated identification of appropriate time
bins. All values associated with labeled bins represent time during experiment in minutes. Only those field
combinations which have met the pre-defined criteria are represented in this table along with sham condition. This
table is complimentary to figure 4.1.
Field Parameters Bin Labels and Time of experiment
Designation
Point Duration Pre-BL Field 1 Field 2a Field 2b Post-BL
Sham
--------------
1-2
2-8
8-16
16-20
20-22 Forward
3
Reversal 1
4.4 Results
4.4.1 Raw Time Series Data:
An example of the raw voltage output simultaneously recorded from both local and
nonlocal beakers is presented in Figure 4.3. Minute fluctuations in the non-local sample's activity
demonstrate a weak positive change in voltage which is associated with the decline in voltage of
the local beaker beginning at approximately min 8 of the experiment. Although this is merely an
example of one trial of one effective field application all other relationships for the effective
fields demonstrated a similar trend.
78
Figure 4.3: Raw outputs of the DC potential of local and nonlocal sources. The example above is one that represents
the 3 msec forward field presentation.
Analyses of variance (ANOVAs) were conducted on the computed mean standardized
Pearson correlation coefficients and revealed no differences in the strength (magnitude) of the
correlation coefficients between independent (isolated bottles) and communal (4L container)
water sources. Additional analyses revealed no interactions between water sources (i.e. isolated
and communal bottles) and point durations (1 and 3 msec) with respect to correlation
coefficients.
Of all the examined combinations of fields only two met the criteria for excess
correlation as outlined in our Methods. The two identified application geometries to elicit excess
correlation were the decelerating-accelerating pattern employing 3 msec point durations, and the
accelerating-decelerating pattern employing 1 msec point durations. A visual representation of
the mean over trials of the within-subjects Z-scored data is presented in Figure 4.4A thru C.
79
Large discrepancies in the initial onset of the measurement can be attributed to one of two things:
1) that each beaker displayed an inverted polarity with respect to the spatially separated beaker
and thus would be reflected in the magnitude and direction of the Z-scored data, and 2) that there
is a temporal latency that is associated with the measurement equipment favouring higher values
at the beginning of the experiment gradually reducing its magnitude throughout the course of the
recording. Even when we accommodate such large discrepancies by within subject
transformations the data remained normally distributed and passed all other statistical
assumptions. Visual comparison of the effective fields to control conditions revealed a
conspicuous discrepancy at the point of intersection between conditions. Figures 4A thru 4C
show that the region of intersection of the DC activity between local and non-local sources in
control conditions occurs at approximately measure 128 which corresponds to a real time of 10
min and 40 sec into the trial. Conversely, the intersection between the effective excess
correlation fields occurs at measure 130. When converted to real time values, this suggests an
effective field homogeny at around min 11, or 3 min into the presentation of the second field.
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Figure 4.4 a-c : Top left hand graph represents the 3 msec field presentation, top right hand graph represents the 1
msec field presentation series whilst the central bottom figure represents the control conditions. All values presented
in the graphs above consist of mean Z-score values within each condition.
A systematic examination of the critical time periods (Table 4.2) associated with the
onset of the different fields was conducted. Results demonstrate an inversion and enhancement
of the Pearson correlation coefficients for the 3 msec decelerating-accelerating and 1 msec
accelerating-decelerating which are classified as the effective field configurations from this
experiment (Figure 4.5). The reversal and enhancement in the magnitude of the Pearson
correlation coefficients persist until approximately min 16 or, consequently, the first 8 min of the
second field application.
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Figure 4.5: Demonstration of the effective application geometries as compared to control conditions. Error bars
reflect calculated standard error measures.
To further identify the temporal window of this phenomenon, min by min investigations were
conducted on the mean Z-score data. Min by min analysis of the two effective application
geometries are presented in Figure 4.6. In this particular instance the duration of the separation
of the correlation coefficients occurs at approximately min 8 and persists to min 12. The 8 to 12
min window corresponds to the first four min of the second field application.
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Figure 4.6: Representation of minute by minute trends in Pearson correlation coefficients with respect of differential
application geometry.
4.5 Discussion
4.5.1 Differences between sources:
These results suggest that despite the samples of water being taken from distinct sources
(separate bottles of water) or were extracted from the same source (4L container of water) there
were no differences identified in the degree of excess correlation. The lack of differences
suggests a level of homogeneity which was exhibited between the spatially separated samples.
Given that both samples were chemically identical, but sampled from different sources (i.e.
containers) they should demonstrate predictable responses to the same applied magnetic fields.
4.5.2 Experimental Phase-shift of DC voltage Inflection:
If one calculates the cycle duration (temporal component of the exposure) of both the 3
msec and 1 msec point durations in their appropriate accelerating or decelerating angular
velocity fields, one can determine the number of elapsed cycles between the initiation of the
83
second field and min 3. The calculated ratio between the number of cycles elapsed of the distinct
3 and 1 msec fields is within the error of 1/2π. The ratio of the number of elapsed cycles between
the distinct point durations may demonstrate an underlying geometry driving the nature of excess
correlation that has yet to be fully investigated. The geometrical relevance of a 1/2π relationship
may correspond to a phase-shift in the temporal duration of the excess correlation effect reducing
the longevity of the excess correlation window.
If one compares the presented results to previous research in the same field one will
notice a temporal phase-shift with respect to the window associated with the peak of excess
correlation. Quantitative support of the excess correlation paradigm suggests that the peak, where
the correlation strength between local and non-local sites is maximized, should occur
immediately after the onset of the accelerating field and persist for 7 to 8 min (Persinger and
Koren, 2015). In contrast to these findings, the duration of the excess correlation window was
determined to be only 4 min in this experiment. It would seem as if the temporal window was
phase shifted, resulting in a temporal delay only lasting 4 min. We posit that the phase
relationship of 1/2 π, demonstrable as the time of intersection of DC profiles of local and non-
local sources, modulates the field by 4 min.
In order for this hypothesis to hold true then the nature of excess correlation must follow
a sinusoidal pattern. By this we suggest that the observed window of 7 to 8 min constitutes the
real portion of a sine wave, which is mathematically described by positive integers between 0
and π radians. The imaginary portion of the wave, that which is mathematically described as the
negative integers between π and 2π, would constitute an unseen portion of the excess correlation
phenomenon. Thus a displacement of 1/2π would necessarily represent a phase-shift in the
sinusoidal nature of excess correlation elongating the imaginary portion of the wave. It would
84
then be theoretically possible to extend and shorten the effective transfer phase (real observable
portion of the excess correlation wave) through direct manipulation. The phase-shift postulate
adheres to the common theories of wave propagation which suggest a phase delay of ¼
wavelength could accommodate for the brevity of the excess correlation window, whilst the
nature of the imaginary portion of the excess correlation wave may represent some form of
storage of the transferred energy or information.
4.5.3 Water Dynamics:
At any interface water demonstrates a more organized structure resulting in an emergent
property capable of excluding a number of solutes within 200 μm of the (Pollack et al., 2009).
The emergence of these solute free regions, termed exclusion zones (EZ), results in a
characteristic alteration of the fundamental properties of water such as electrical potential,
viscosity and pH (Pollack et al., 2009). For instance, the potential difference of water
demonstrates a 100 to 200 mV gradient separating interfacial water from bulk water (Pollack et
al., 2008). Assuming that the electrical potential gradient separating bulk water from interfacial
water is maintained by a single charge, and given that a charge has a value of 1.602∙10-19 A∙s,
then the energy required to maintain the gradient between bulk and interfacial water would be
the product of the unit charge and the potential difference, which results in a value of 2.4∙10-20
Joules (kg∙m2∙s-2). Energy in the order of 10-20 Joules converge upon the fundamental operational
energy of the cell (Persinger, 2014c).
Another physical property of interfacial water is its relatively low pH and the ability for
protons to move across the electrical potential gradient. The movement of protons from the EZ
can influence the pH of the surrounding bulk water so long as the EZ builds or expands. The
process of building the EZ is influenced by the intensity and exposure length of external energy
85
(Chai et al., 2009). Thus the application of electromagnetic fields provides energy to the system
and induces a change in the electric field of the material. A consequence of this alteration in the
electric field is a change in the magnitude of the electrical potential gradient allowing for the
expansion of the EZ and increasing the movement of protons from EZs to bulk water. The
application of the appropriate time-varying magnetic fields liberates contained hydronium ions,
altering the pH of the solution. Although we did not directly measure the pH of the solution, we
may be able to infer the pH from the electrical potential (Alberts et al., 2002). A general
hypothesis for the duration of the 4 min may be accommodated by the expansion of the EZ. The
terminal velocity of expansion resulting in the formation of an extended EZ may reduce the
temporal window for excess correlation. Modulation of the EZ has been accomplished by Chai et
al. (2010) by applying 3.4 μm wavelength light (infrared) for 10 min. The effects of the light
stimulation resulted in a net increase of the water's temperature by 1℃ and increased the
boundary of the EZ by a factor of 4 in a volume of 5 mL.
Given that the specific heat of water in its liquid form is 4.184 J∙g-1K-1 the total amount of
energy donated to the system was approximately 20.92 J. Given that the energy required to grow
theEZ is 20.92 Joules and that 5 mL of water contains 1.67∙1023 molecules, the net energy per
molecule is 1.19∙10-22 J/molecule. The amount of energy provided to the system through the
application of the time-varying magnetic fields can be calculated using the equation:
Equation 4.1: J = ∙V
where J is the energy in Joules, B is the strength of the applied magnetic field in Tesla (kg∙A-1∙s-
2), V is the volume of space affected and 2µ is the permeability of free space. In our experiment,
the application of a 3 nT field in the volume of a 25.4 cm diameter toroid results in an energy of
1.79∙10-16 J. If we take the total energy impacting the system and the energy associated with the
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expansion of the EZ (1.19∙10-22 J/molecule) the total number of water molecules affected was
approximately 1.50∙106 molecules.
The baseline formation of the EZ extends approximately 200 μm, and when subjected to
a 4 fold increase in size (to 800 μm) the resultant change in the size of the EZ is approximately
600 μm per 10 min. If we consider the change in the size of the EZ to occur isotropically in all
directions, we can then calculate the diffusion velocity for the expansion. Here if the change in
size of the exclusion zone is 600 μm over 10 min the diffusion velocity would be in the order of
6.0∙10-10m2∙s-1. Given that our window of excess correlation is 4 min in duration then the degree
of linear expansion would be approximately 3.79∙10-4m. If the intramolecular separation between
an H-atom and an O-atom in water is 0.0978∙10-9 m (Bieze et al., 1993), then the approximate
length of the entire molecule would be 1.96∙10-10 m. The total number of molecules which are
recruited to the EZ through its expansion would be the quotient of the distance expanded and the
linear distance of a water molecule, resulting in 3.06∙106 molecules. These results converge at the
same magnitude and mimic the number of ions responsible for maintaining the electrical
potential of the cell membrane (Persinger, 2014c).
4.5.4 Further Quantitative Support:
The results obtained in this experiment corroborate other findings suggesting that applied
magnetic fields can elicit excess correlation (Dotta and Persigner, 2012; Dotta et al., 2009; Burke
et al., 2013; Dotta et al., 2013; Rouleau and Carniello, 2014). However, it has introduced an
occult variable that has otherwise gone unnoticed. The overlap between the appropriate order of
decelerating and accelerating angular velocity magnetic fields with 3 msec point durations may
reflect the discrete fluctuations of hydronium dynamics. The generation of interfacial EZs is
theoretically sufficient at elucidating the demonstrated effects. Identifying the basic
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characteristics of an inverted field presentation with 1 msec point durations as an effective field
geometry does not resonate with current hypotheses of mechanism. Indeed the structure of the
presentation has altered but the effect remains the same suggesting that the destabilization of
bulk group velocity as a theoretical mechanism may still apply
Brushci and colleagues (2014) proposed an experiment whereby the manipulation of the
acceleration of entangled particles may enhance or degrade the phenomenon. The alteration of
physical acceleration can be considered a disruption in local gravitational effects. Although the
proposed experiment relies on the physical acceleration of the system to elicit said effects, one
may apply appropriate magnetic fields to elicit similar results. It has been proposed that in higher
dimensions of space-time interactions between electromagnetic fields and gravitational fields
occur, suggesting that this interaction between gravity and electromagnetism can be observed in
Minkowski space-time (Kaluza, 1921).
Consider gravitational effects to be described as an accelerated frame and that the effects of
electromagnetism on gravitational alterations can be measured in local space-time. Treating
gravity as an acceleration we can then relate the gravitational force to the electromagnetic force
via the relationship of:
Equation 4.2: m∙a = ∙A
where m is the mass, a is the acceleration, B is the intensity of the magnetic fields, μ is the
permeability of free space and A is the area the field occupies.
The acceleration component of the magnetic fields can be calculated as described by the
equation:
Equation 4.3: a =
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where a is the acceleration, Δv is the change in velocity and Δt is the change in time.
However the velocity around a circle is calculated as 2πr/T, where r is the radius of the circle and
T is the period. For our applied magnetic fields the acceleration around a 25.4 cm diameter with
a change in time of 2 msec approaches 104m∙s-2. Rearranging Equation 4.1 to solve for the
intensity of the field required to elicit responses in protons with a mass of 1.6∙10-27 kg and
electrons with a mass of 9.11∙10-31 kg solves to a required intensity range of 10-28 to 10-34 T.
Given that the intensities of the applied fields were ~0.03 mG (or the equivalent of 3.0∙10-9 T)
and if we assume that the limit of 10-28 to 10-34 T represents the necessary field required to
disturb a single particle, then the total number of particles that can be affected range from 1019 to
1025 particles. These values represent the possible number of protons and electrons which may be
respectively involved in this process. Furthermore, if one utilizes the calculation for coherence
between the time of weakly dissipative systems (Bush and Parentani, 2013) in Equation 4.4 one
can calculate the mass associated with the 8 min window of the excess correlation effect:
Equation 4.4: t = ħ/(mc2)
where t is the coherence time, ħ is reduced Planck's constant, m is the mass and c is the speed of
light. When one isolates the effective mass component of Equation 4.3 and solves for its value,
given that the approximated temporal relationship of the excess correlation design is in the order
of 102 sec, the value of the mass is effectively 10-52 kg. This value represents the upper limit of
the rest mass of the photon as determined by Tu and colleagues (Tu et al, 2005).
If the absolute duration of the excess correlation phenomenon is contingent upon photon
and electron activity then this may indirectly support the notion of a coupling between the
electron and the photon. Conceptually we can envision the positron as being a composite of the
superposition of both an electron and photon and thus can be described, with respect to the
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principles of quantum electrodynamics, as an electron traveling backwards in time (Feynman,
1949). Considering this definition of electron-photon coupling it could be argued that the reverse
presentation of differential accelerating and decelerating angular velocity fields could elicit this
effect through positrons. This argument can be justified in the sense that the calculated effective
values for the electron are retained even if the electron is assumed to be a positron. Furthermore,
the addition of any external force or stimulus would fix the linearity of causality and allow for
the elicitation of effects via the classical electron model rather than through the activity of a
positron node.
Based on the criteria established in this document it can be demonstrated that the
application of an accelerating angular velocity field followed by a decelerating angular velocity
field containing 1 msec point durations can elicit the excess correlation effect without the
presumed necessary application of an exogenous stimulus. Similar results can are demonstrated
with the application of decelerating angular velocity field followed by the application of an
accelerating angular velocity field only when using 3 msec point durations as demonstrated in
Figures 4.5 and 4.6. The conspicuous nature of these results show that an external stimulus is not
required to elicit excess correlation. We have demonstrated the possibility of passive excess
correlation between spatially separated samples water that share specific magnetic field
configurations, however the mechanism by which these alterations in electrical potential occur
has not been determined. We suggest that the dynamics of water at an interface may contribute to
the overall phenomenon of excess correlation with or without the application of an exogenous
proton donor.
90
4.6 References:
Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 4th edition. New York:
Garland Science; 2002.
M.C. Arnesen, S. Bose, V. Vedral Natural thermal and magnetic entanglement in the 1D
Heisenberg model Phys. Rev. Lett., 87 (2001) 017901(1)-4.
Bieze, T. W. N., van der Maarel, J. R. C., & Leyte, J. C. (1993). The intramolecular OH bond
length of water in a concentrated poly (ethyleneoxide) solution. An NMR relaxation
study. Chemical physics letters,216(1), 56-62.
Burke, R. C., Gauthier, M. Y., Rouleau, N., & Persinger, M. A. (2013). Experimental
demonstration of potential entanglement of brain activity over 300 Km for pairs of
subjects sharing the same circular rotating, angular accelerating Magnetic fields:
verification by s_LORETA, QEEG measurements. Journal of Consciousness Exploration
& Research, 4(1).
Busch, X. and Parentani, R. Phys. Rev. D 88, 045023 (2013).
Bruschi, D. E., Sabín, C., White, A., Baccetti, V., Oi, D. K., & Fuentes, I. (2014). Testing the
effects of gravity and motion on quantum entanglement in space-based experiments. New
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Chai, B., Yoo, H., & Pollack, G. H. (2009). Effect of radiant energy on near-surface water. The
Journal of Physical Chemistry B, 113(42), 13953-13958.
Dotta, B. T., Mulligan, B. P., Hunter, M. D., & Persinger, M. A. (2009). Evidence of
macroscopic quantum entanglement during double quantitative electroencephalographic
measurements of friends vs. strangers. NeuroQuantology, 7(4), 548-551.
Dotta, B. T., Buckner, C. A., Lafrenie, R. M., & Persinger, M. A. (2011). Photon emissions from
human brain and cell culture exposed to distally rotating magnetic fields shared by
separate light-stimulated brains and cells. Brain research, 1388, 77-88.
Dotta, B. T., & Persinger, M. A. (2012). “Doubling” of local photon emissions when two
simultaneous, spatially-separated, chemiluminescent reactions share the same magnetic
field configurations. Journal of Biophysical Chemistry, 3(01), 72.
Dotta, B. T., Koren, S. A., & Persinger, M. A. (2013). Demonstration of entanglement of “pure”
photon emissions at two locations that share specific configurations of magnetic fields:
implications for translocation of consciousness. Journal of Consciousness Exploration &
Research, 4(1).
Dotta, B. T., Murugan, N. J., Karbowski, L. M., & Persinger, M. A. (2013). Excessive correlated
shifts in pH within distal solutions sharing phase-uncoupled angular accelerating
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magnetic fields: Macro-entanglement and information transfer. Int J Phys Sci, 8, 1783-
1787.
Feynman, R. P. 1949a. "The Theory of Positrons."Phys. Rev. 76, pp. 749-759.
Hill, S., & Wootters, W. K. (1997). Entanglement of a pair of quantum bits. Physical review
letters, 78(26), 5022.
Hoffmann, J., Krug, M., Ortegel, N., et al. (2012) Heralded Entanglement between Widely
Separated Atoms. Science, 337, 72-75. http://dx.doi.org/10.1126/science.1221856
Kaluza, Theodor (1921). "Zum Unitätsproblem in der Physik". Sitzungsber. Preuss. Akad.
Wiss. Berlin. (Math. Phys.): 966–972.
Persinger, M. A., & Koren, S. A. (2007). A theory of neurophysics and quantum neuroscience:
implications for brain function and the limits of consciousness. International Journal of
Neuroscience, 117(2), 157-175.
Persinger, M. A., Tsang, E. W., Booth, J. N., & Koren, S. A. (2007). Enhanced Power within a
Predicted Narrow Band of Theta Activity During Stimulation of Another By
Circumcerebral Weak Magnetic Fields After Weekly Spatial Proximity: Evidence for
Macroscopic Quantum Entanglement?. NeuroQuantology, 6(1).
Persinger, M. A. (2013). Support for Eddington’s Number and his approach to astronomy: recent
developments in the physics and chemistry of the human brain. International Letters of
Chemistry, Physics and Astronomy, 8.
Persinger, M. A., & Koren, S. A. (2013). Dimensional analyses of geometric products and the
boundary conditions of the universe: implications for a quantitative value for the la-
teensy to display entanglement. Open Astronomy Journal, 6, 10-13.
Persinger, M.A. (2014). Discrepancies between predicted and observed intergalactic magnetic
field strengths from the universe’s total energy: is it contained within submatter spatial
geometry? International Letters of Chemistry, Physics and Astronomy, 11, 18-23.
Persinger, M. A. (2014b). Quantitative convergence between physical-chemical constants of the
proton and the properties of water: Implications for sequestered magnetic fields and a
universal quantity. Physics and Astronomy, 2, 1-10.
Persinger, M. A. (2014c). Astronomical, chemical, and biological implications of 10-20 joules as
a fundamental quantum unit of information for neurofunction. Archives of
Neuroscience, 1(3).
Persinger, M. A., & Koren, S. A. (2015). Potential role of the entanglement velocity of 1023 m∙ s-
1 to accommodate recent measurements of large scale structures of the
Universe. International Letters of Chemistry, Physics and Astronomy, 3, 106.
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Pollack, GH; Clegg, J. Unsuspected Linkage Between Unstirred Layers, Exclusion Zones and
Water. Phase Transitions in Cell Biology; Pollack, GH, Chin, WC, Eds.; Springer: New
York, USA, 2008; p. 183.
Pollack, G. H., Figueroa, X., & Zhao, Q. (2009). Molecules, water, and radiant energy: new
clues for the origin of life. International journal of molecular sciences, 10(4), 1419-1429.
Rouleau, N., Carniello, T. N., & Persinger, M. A. (2014). Non-local pH shifts and shared
changing angular velocity magnetic fields: Discrete energies and the importance of point
durations. Journal of Biophysical Chemistry, 2014.
Tu, L. C.; Luo, J.; Gilles, G. T.(2005).The mass of the photon. Rep.Prog. Phys. 68, 77-130.
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Chapter 5
5 Interaction of Bulk Angular Velocity Electromagnetic Fields and
Temperature Variations on the Latency of pH shifts
5.1 Abstract
We investigated the potential interaction between temperature variations and bulk
accelerating or decelerating electromagnetic fields on temporally delayed, decreased magnitude
shifts in pH in response to weak acids. The appropriate combination of near 0⁰C temperatures
and an accelerating bulk angular velocity electromagnetic field with 1 msec point durations
maximized the reduction in absolute shifts of pH in response to the injection of weak acids in a
local beaker. However, the application of near 40⁰C temperatures exacerbated the delayed
response of water to serial injections of weak acids which had been exposed to a 3 msec
decelerating bulk angular velocity electromagnetic field configuration. The latter configuration
of applied electromagnetic fields has been successful in reducing the degree of pH shifts in the
past. The gravitational-electromagnetic equivalence across the temporal duration of the Universe
is explored as a potential mechanism for the observed effect.
5.2 Introduction
Water has the distinct characteristic of altering its physical properties at an interface. The
resultant differentiation into two distinct physical entities has been termed bulk and interfacial
water, respectively (Fenn et al., 2009). Invariably, properties such as viscosity, proton dynamics
and even electrical potential are enhanced by the influence of uncharged, polar surfaces (Chen et
al., 2007; Zheng et al., 2006; Henderson, 2002; Thiel et al., 1987; Chai et al., 2012).
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Furthermore, an enhancement of the physical properties of water at an interface creates a zone at
which solutes, including some ions, are excluded from interfacial water (Yoo et al., 2011). These
exclusionary properties, also termed the exclusion zones of water, have been remarkably
implicated with the intricacies of the formation of primordial membranes (Trevors and Pollack,
2012).
Recall that the ultimate function of the cell membrane, the primary constituent of all living
organisms, is its ability to selectively filter required molecules and ions for the generation and
maintenance of the electrical equilibrium responsible for maintaining the integrity of the cell.
The exclusion zone and associated its interfacial water have been metaphorically approached as a
static equilibrium and has been presented as such in the academic literatures. However, liquid
water is not a static solution, as the molecules are in a constant state of flux. Furthering this
claim, water has the unique ability to temporarily form hydronium ions which demonstrate a
relatively short existence (on the picosecond scale, Persinger, 2014). In corroboration with these
presented ideas Chai and Pollack demonstrated the potential of expanding the influence of the
exclusion zone via irradiation with 3.4 μm wavelength light (Chai and Pollack, 2010). This may
lend credence to the notion that the exclusion zone of water is not actually static and
demonstrates its dynamic potential to be influenced by physical manipulation. Thus the potential
then arises for one to examine this dynamic nature of the interfacial aspects of water and its
associated exclusion zone in further detail. Chai and Pollack have identified that the effects of
incident radiation resulted in a differential increase of the measured temperature of the solution
by 1 C. The alteration in the ambient temperature of the solution was accompanied by an
expansion of the exclusion zone (Chai and Pollack, 2009). In this manner the practical
95
investigation would be to examine the influence of temperature on a potential dynamic measure
of the exclusion zone or its associated processes.
Here we suggest that the simple observation of alterations in pH measurements should
suffice as an approximate measure of the dynamic nature of the exclusion zone. Intuitively, if
one injects a solution with an acid (a proton donor) the pH of the solution should acidify,
however in an area which is surrounded by interfacial water deviation in pH recordings should
reflect a marked delay in response to the stimulus or an overall reduction in the magnitude of the
shift. Holding effects in pH are transient events that present as a reduction, or temporal dilation,
in the absolute magnitude of the recorded pH as has been demonstrated by Murugan and
colleagues (Murugan et al., 2015). In the Murugan et al. (2015) studies, water which was
exposed to patterned electromagnetic fields demonstrated a conspicuous slowing of the onset of
pH shifts when injected with a proton donor. In this particular context, the mechanism was akin
to the strengthening of the exclusion zone of water. The combination of the appropriate
electromagnetic fields could thus enhance the expansion of the exclusion zone to a point which
would also expand the temporal window of reduction in pHs shifts. The net temporal delay in the
onset of deviations in pH would be akin to increasing the effective range of the exclusion zone
and should be investigated in greater detail. To define the causal relationship between the
potential holding properties of water and the degree of reduction in absolute pH shifts or the
onset of said shifts, its interaction with applied electromagnetic fields and temperature variations
must be investigated to understand the potential mechanistic formation of primordial substrates
similar to our now-defined cell matrices.
5.3 Materials and Methods
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Open 250 mL beakers containing 25 mL of PC Brand Spring Water (Gray County Ontario)
were subjected to a series of injections of 50 μL of 5% v/v acetic acid. 9 min of continuous data
were sampled at 1 Hz increments using a Dr. DAQ pH recording device and associated pH
sensor probe, while simultaneous measures of temperature were monitored each min using a
mercury thermometer (range - 20⁰C to 100⁰C). Of the 9 min continuous data set the first 2 min
segment was designated as a pre-exposure baseline measure, followed by 5 min of weak
intensity bulk accelerating and decelerating angular velocity fields with 50 μL injections of 5%
acetic acid. Injections occurred once every min for the duration of the 5 min field exposure. The
final 2 min of the recording was followed by the immediate cessation of the field but not the
removal of the sample from the exposure apparatus and constituted post-exposure measures.
5.3.1 Field Parameters and Exposure Apparatus:
The field exposure apparatus consisted of a single toroid constructed out of a 25.4 cm
diameter plastic hoop, which was wrapped 216 times with 22 gauge insulated copper wire. Field
exposure parameters consisted of either an accelerating bulk angular velocity field or a
decelerating bulk angular velocity field. Each respective accelerating and decelerating field
consisted of either 1 or 3 msec point durations, respectively. In this particular instance the
concept of point durations is the specified length of time that a current will be passed into the
exposure apparatus resulting in the production of transient (time-varying) electromagnetic fields.
The accelerating and decelerating component of the resultant magnetic fields were generated by
serially modulating the delay between point durations and is referred to as the inter-stimulus
delay. For the accelerating bulk angular velocity field the initial inter-stimulus delay was set to
20 msec and was serially decreased by 2 msec from its initial value to a final value of 8 msec.
Similarly, the decelerating angular velocity field had an initial inter-stimulus delay of 20 msec
97
which was serially increased by 2 msec to a final value of 32 msec. Each iteration of the
appropriate increase or decrease in inter-stimulus delay was followed by the appropriate 1 or 3
msec point durations. Both the accelerating and decelerating bulk angular velocity fields were
continuously looped for the entire 5 min exposure duration. Here, the modulation of both the
inter-stimulus delays and point durations were accommodated by an Arduino Uno
microcontroller and associated software. Intensities of the resultant electromagnetic fields were
0.03 mG (3.0 nT) which were verified using an AC milliGauss meter (model UHS, AlphaLabs,
Inc USA).
5.3.2 Modulation of Temperature Variables:
For this experiment there were three different temperature conditions (Room
Temperature, Cold and Hot) which were subjected to three field conditions (Sham, Accelerating
and Decelerating) consisting of two different point durations (1 and 3 msec points), with time as
a within subjects measure. The Sham field condition comprised the same experimental procedure
(i.e. placing the open beaker in the toroid and monitoring the pH and temperature variations over
the course of 9 min) without the fields being turned on. For the room temperature condition the
water samples never exceeded the ambient temperature of the testing room (23 ± 1⁰C).Cold and
hot conditions were accommodated by placing the 250 mL open beaker into a 1 L (containing
300 mL of water or ice water) ice bath or double boiler and hot plate until a temperature of 8⁰C
and 35⁰C were reached in each respective condition. In these cases the 1 L beaker was removed
from the hot plate, or cooling location, and placed inside the toroid. All hot and cold conditions
did not deviate more than ± 6⁰C of their starting temperatures of 35⁰ and 8⁰C respectively.
5.3.3 Statistical Methods:
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All raw recorded data was imported into SPSS 17 statistical software for transformations
and subsequent data analysis. The arithmetic means from the raw records of all trials sampled at
1 Hz increments were aggregated into 1 min bins. A relative change in pH from the computed
mean values of the 1 min bin was calculated over the course of the experiment. The resultant
computed relative changes in pH were subsequently within subjects Z-scored within the time
domain before being subjected to further statistical analyses.
5.4 Results
A repeated measures analysis of variance (ANOVA) was conducted with time of injection
as a within-subjects factor and revealed a significant 3-way within-subjects interaction of field
by temperature by time [F(56,217) = 1.68, p<.01, partial η2 = 0.30] which are displayed in Figures 1
thru 3.
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Figure 5.1: Room temperature events. In this condition the effects of 3 msec decelerating field was most effective to
hold pH over time while 1 msec accelerating field reduced overall shifts in pH after 9 minutes. Error bars are
measures of SEM. ACC denotes accelerating field parameters whilst DEC represents the decelerating field
parameters.
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Figure 5.2: Cold condition, approximately 0⁰C, demonstrative that 1 msec accelerating field reduces the overall
change in pH over time. Error bars are measures of SEM. ACC denotes accelerating field parameters whilst DEC
represents the decelerating field parameters.
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Figure 5.3: : Hot condition, 1 msec accelerating field effects are reduced and holding occurs for only 2 minutes.
Error bars are measures of SEM. ACC denotes accelerating field parameters whilst DEC represents the decelerating
field parameters.
Supplementary post-hoc ANOVAs were conducted in order to discern the net driving
factor behind the 3-way interaction. The within subject standardized relative shift in pH during
min 2 for room temperature sham, room temperature 3 msec decelerating, and room temperature
accelerating field presentations as well as sham field exposure during cold conditions did not
differ amongst themselves however were all were significantly different from 1 msec
decelerating cold parameters which had a less significant (towards alkalinity) shift. Min 3
differences were accommodated by a greater drop in the within-subjects standardized relative pH
for 3 msec accelerating bulk angular velocity field at high temperatures as compared to the 1
msec decelerating cold temperature condition. This trend continued into min 4 however the 1
msec accelerating field condition in the cold temperatures was not different from the 1 msec
decelerating cold condition. In min 5 the only significantly different changes in pH were between
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the 3 msec accelerating high temperature condition and the 1 msec accelerating cold condition.
The latter of which produced a reduction in the within subjects standardized relative shift in pH
(less acidic) whilst the former enhanced the change in pH towards acidity. Min 6 showed that the
3 msec accelerating high temperature, 3 msec decelerating high temperature and 1 msec
decelerating room temperature conditions did not differ in the degree of the magnitude of the
within-subjects standardized relative pH but however they were significantly different from the 1
msec accelerating cold condition which displayed a reduction in the within-subjects relative
change in pH. The same trend which was identified in min 6 of the experiment was also
identified in min 7 of the experiment. The final 2 min shared the same driving factors which
identified the 3 msec hot decelerating condition as having a much lower pH (greater negative
shift in the within-subjects standardized relative change in pH) as compared to cold water
samples which were exposed to 1 msec accelerating bulk angular velocity fields.
5.5 Discussion
Here we have presented a myriad of data supporting a complex interaction between
electromagnetic fields and temperature on standardized relative changes in the pH of water
subjected to injections of a proton donor. This interaction can be labeled as a pH "holding" effect
which our lab has previously identified as being associated with the application of weak intensity
magnetic fields consisting of 3 msec point durations (Murugan et al., 2015 and unreported
results). We have also demonstrated that varying the temperature of the immediate environment
without the appropriate application of magnetic fields does not have an effect on the change in
pH as measured by our equipment. Thus the direct implication of temperature as a moderator of
the organization and expansion of the exclusion zone cannot, under these conditions, explain the
dynamic processes associated with the pH holding effect and suggests an alternative mechanism
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to this expansion as denoted by Chai and Pollack (2010). Of note, however, is the reduction in
the efficacy of the pH holding effect at approximately 40⁰C when exposed to a 3 msec
decelerating field at which had otherwise produced a reduction in the relative change in pH.
Our most conspicuous effects presented in this report were the marked reduction in the
relative change in pH at low temperatures (cold) while exposed to 1 msec accelerating bulk
angular velocity fields. Remarkably, the reduction in the relative change in pH in this particular
condition was greater than any other condition investigated. A possible explanation for the
observed effect is akin to the strengthening of the organization of the water molecules within the
exclusion zone. Thus, we endeavour to mathematically verify the potential of a 1 msec
accelerating bulk velocity field at approximately 0⁰C to affect the underlying structure of the
water molecules in 25 mL of spring water. One assumption of this mathematical interpretation is
centered on the notion that structured organization of any substance is directly related to the
underlying space-time geometry which the matter influences.
In this instance, the curvature (geometry) of space-time is not solely affected by gravity but
is also influenced by the magnetic properties of the contained matter. Thus, the geometric
representation of the influence of water on space-time is represented by the Einstein-Maxwell
relationship:
Equation 5.1: R = [ εE2 + B2]
where R is the curvature (m-2), G is the gravitational constant (6.67∙10-11 m3∙kg-1∙s-2), ε is the
permittivity of free space 8.85∙10-12 A2∙s4∙kg-1∙m-1), μ is the permeability of free space (1.256∙10-6
kg∙m∙A2∙s2), E is the electric field (kg∙m∙A-1∙s-3) and B is the magnetic field (kg∙A-1∙s-2). For
water at 0⁰C, the thermodynamic energy (entropy/structured energy) is 75.97 J∙mol-1K-1.
Therefore, 25 mL of water (1.39 moles) would have a thermodynamic equivalent energy of
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2.88∙104 J (kg∙m2∙s-2) at 0⁰C. Given the total number of water molecules stored in 25 mL of water
is 8.37∙1023 molecules and the electric dipole moment of water is 6.16∙10-30 As∙m (Clough et al.,
1973) the total electric dipole of the solution would be 51.55∙10-7As∙m. Dividing the
thermodynamic energy of all the water molecules stored within the solution by the net dipole
moment of all the water molecules would yield an electric field strength of 5.58∙109 kg∙m∙A-1∙s-3
(V/m).
Equivalently, the squared magnetic field strength of water can be approximated by using
the equation B2 = [J∙2 u]/V, where V is the volume of water (25 mL = 2.5∙10-5 m3) in cubic
meters. The resultant squared magnetic field strength would be 2.88∙103 kg2∙A-2∙s-4 (T2).
Inserting the value of the squared magnetic field strength and the electric field into the Einstein-
Maxwell equation yields a value of 1.27∙10-27m-2, the inverse of which is 7.87∙1026m2. From here
we can calculate a time component associated with the structure of 0⁰C water using a magnetic
diffusivity constant. Given that the average conductivity of water is 1.75uS∙cm-1 and that the
magnetic diffusivity is calculated using the relationship 1/µσ, where μ is the permeability of free
space and σ is the conductivity of water, the value approaches 109m2∙s-1. Dividing the inverse of
the Einstein-Maxwell geometry by the magnetic diffusivity of water results in a value of 1017
sec. This suggests that the structure of water at 0⁰C converges at the level of the age of the
Universe in regards to the magnetic diffusivity. That is, the time it would take for that magnetic
equivalent of water to diffusive across the inverse of its own curvature is the age of the universe.
This implies that the 1 msec effects of an accelerating field may homogenize the structure of
water such that the proton diffusion is smeared across space-time and supports the theories of
(Wheeler, 1946).
105
Conversely, if one divides the value of obtained from the Einstein-Maxwell equation
(1.27∙10-27m-2) by the magnetic diffusivity of water (4.57∙109m2∙s-1), the resultant temporal
duration would be in the order of 2.77∙10-37 sec or the equivalent of 3.60∙1036 Hz. Given that the
energy of this frequency can be calculated using Planck's relationship E = hv, where E is the
energy, h is Planck's constant 6.626∙10-34 kg∙m2∙s-1, it yields an energy in the order of 2.38∙103
kg∙m2∙s-2. Dividing this resultant energy by the total number of available water molecules results
in an energy of 2.85∙10-21 kg∙m2∙s-2 per molecule. When one calculates the Landauer limit for
0⁰C water using the relationship S = kbT ln2, where kb is Bolztmann's constant (1.28∙10-23
kg∙m2∙s-2∙K) and T is the temperature in degrees Kelvin, the value obtained approaches 2.6∙10-21
kg∙m2∙s-2. That is to say that the energy equivalent of the magnetic diffusivity of water at 0⁰C is
within the error of limit of energy required to transform one bit of information.
Furthermore, a theoretical inference of the efficacy of the presentation of the accelerating
field parameters may be an induction of the stretching and compression of the hydrogen bonds
and other associated molecular bonds of water. As water is cooled to 4⁰C, the density of the
aqueous state is increased, thus resulting in a decrease in the separation of the bonds. However,
as the liquid state approaches the limit of 0⁰C on its transition to a solid state the density is
reduced. A reduction in the density of liquid water upon its transition from 4⁰C to 0⁰C increases
the bond length between the atoms alongside the reduction in the hydrogen bonding potential.
The net effect upon transitioning into these lower temperatures is a condition that is similar to an
initial compression and stretching of the molecules. In this instance the resultant stretch after the
transition between 4⁰C and 0⁰C is akin to the tangential effects of a centripetal acceleration
whereby a tethered mass is displaced outwardly. The tangential displacement of the molecules
that underlie the structure of water synchronize with the applied accelerating field resulting in a
106
form of resonance. The homogeneity driven by the accelerated frame not only results in the
appropriate enhancement of the pH holding effects, but also agrees with the architecture of
general relativity which suggests gravity can be modeled as an accelerated frame (Einstein, 1952;
Einstein, 1921). Furthermore, the presented theory is corroborated by the interaction of the
electron comprises the molecular bonds of the solution and the convergence of its activity in the
1 msec temporal window which has been presented elsewhere (Persinger and Koren, 2007).
107
5.6 References :
Chai, B., Yoo, H., & Pollack, G. H. (2009). Effect of radiant energy on near-surface water. The
Journal of Physical Chemistry B, 113(42), 13953-13958.
Chai, B., & Pollack, G. H. (2010). Solute-free interfacial zones in polar liquids. The Journal of
Physical Chemistry B, 114(16), 5371-5375.
Chai, B., Mahtani, A. G., & Pollack, G. H. (2012). Unexpected presence of solute-free zones at
metal-water interfaces. Contemporary materials, 3(1), 1.
Chen, X., Yang, T., Kataoka, S., & Cremer, P. S. (2007). Specific ion effects on interfacial water
structure near macromolecules. Journal of the American Chemical Society, 129(40),
12272-12279.
Clough, S. A., Beers, Y., Klein, G. P., & Rothman, L. S. (1973). Dipole moment of water from
Stark measurements of H2O, HDO, and D2O. The Journal of Chemical Physics, 59(5),
2254-2259.
Einstein, A. (1921). A brief outline of the development of the theory of relativity. Nature,
106(2677), 782-784.
Einstein, A. (1952). The foundation of the general theory of relativity. The Principle of
Relativity. Dover Books on Physics. June 1, 1952. 240 pages. 0486600815, p. 109-164, 1,
109-164.
Fenn, E. E., Wong, D. B., & Fayer, M. D. (2009). Water dynamics at neutral and ionic interfaces.
Proceedings of the National Academy of Sciences of the United States of America,
106(36), 15243–15248. doi:10.1073/pnas.0907875106
Henderson, M. A. (2002). The interaction of water with solid surfaces: fundamental aspects
revisited. Surface Science Reports, 46(1), 1-308.
Murugan, N. J., Karbowski, L. M., Dotta, B. T., & Persinger, M. A. (2015). Delayed shifts in pH
responses to weak acids in spring water exposed to circular rotating magnetic fields: A
narrow band intensity-dependence. International Research Journal of Pure and Applied
Chemistry, 5(2), 131.
Persinger, M. A., & Koren, S. A. (2007). A theory of neurophysics and quantum neuroscience:
implications for brain function and the limits of consciousness. International journal of
neuroscience, 117(2), 157-175.
108
Persinger, M. A. (2014). Quantitative convergence between physical-chemical constants of the
proton and the properties of water: Implications for sequestered magnetic fields and a
universal quantity. International Letters of Chemistry, Physics and Astronomy, 2, 1-10.
Thiel, P. A., & Madey, T. E. (1987). The interaction of water with solid surfaces: fundamental
aspects. Surface Science Reports, 7(6), 211-385.
Trevors, J. T., & Pollack, G. H. (2012). Origin of microbial life hypothesis: A gel cytoplasm
lacking a bilayer membrane, with infrared radiation producing exclusion zone (EZ)
water, hydrogen as an energy source and thermosynthesis for bioenergetics. Biochimie,
94(1), 258-262.
Wheeler, J. A. (1946). Problems and prospects in elementary particle research. Proceedings of
the American Philosophical Society, 36-47.
Yoo, H., Paranji, R., & Pollack, G. H. (2011). Impact of hydrophilic surfaces on interfacial water
dynamics probed with NMR spectroscopy. The journal of physical chemistry letters, 2(6),
532-536.
Zheng, J. M., Chin, W. C., Khijniak, E., & Pollack, G. H. (2006). Surfaces and interfacial water:
evidence that hydrophilic surfaces have long-range impact. Advances in colloid and
interface science, 127(1), 19-27.
109
Chapter 6
6 Quantitative Support for Water as the Conduit of Interaction between the
Immaterial (Energy) and Matter: The Necessary Contingencies for
Universal Equivalence
We have successfully demonstrated the potential role of water in the onset of senescence
and the excess correlation between two spatially isolated and phylogenically isolated species.
Furthermore, we have directly investigated the electrodynamic properties of excessively
correlated water and the dynamic nature of the exclusion zone. In Burr's original hypothesis the
Blueprint for Immortality was stored within the electric fields of the organisms which he
investigated. Recall the supposition that energy is transient and fluid in nature with a level of
fragility that accompanies its structure. Energy requires a necessary structural analog in order for
it to persist over a larger temporal span. However, the structure must also be malleable in order
for modifications and additions to be effectively represented in the long term. Structure without
energy or a driving process is static and does not allow for the propagation of the system over an
extended period of time. It is the interaction between matter and energy that reflects the
indeterminate longevity of the unit (organism). The more complex the unit becomes, the more
specialized the structure, the less the longevity. The rigidity of the inherent, correlated structure
must not exceed a certain threshold in order for it to pervade large expanses of time. Here we do
not exclude the thesis of Burr but aim to synthesize structure and process. The physical
properties of water reflect a necessary conduit for the generation of complex electrodynamic
processes reflective of the dynamic nature of the structure from which it is formed.
We have implicated water as a central moderator for the interactions of plants and bacteria
and have also implied a photon-water mechanism for the effective elicitation of passive excess
110
correlation. In the latter example the water does not require the influence of an external stimuli
allowing for the potential exchange of information over vast distances. Similarly, the influence
of light on water, at least with respect to senescence, demonstrates an underlying property of the
nature of the interaction of light with water. Although differences were obtained across time for
each successive stage of senescence, generally plants did not respond in an intensity dependent
manner to incident radiation. We hypothesized that a residual biological or non-biological agent
may be mediating the observed alterations in electrical potential. Furthermore, the lack of
intensity dependence in this particular instance could also suggest a photo-electric phenomenon
whereby frequency, pattern and intensity are required in order to maximize the observed changes
in electrical potential. It is suggested that optimal wavelengths associated with the activity of
photosystems I and II be applied with peak wavelengths of water and 3.4 μm waves. The
application of the aforementioned wavelengths of light would narrow the spectrum of
observation and illuminate potential sources driving the observed effect. Finally interactions with
the exclusion zone, as measured by changes in the latency and magnitude of pH shits, when
exposed to the appropriate configuration of magnetic fields is of interest and has been discussed
at length. The most probable common denominator of all the experiments lies within the realm of
water and every experiment should reflect a continuity of the properties of water even in distinct
levels of discourse. If water is the source of the Blueprint for Immortality and is the central
moderator for the effects observed here, then its physical properties should be reflected not only
across multiple levels of discourse but also precipitate as a central variable on the scale of the
Universe.
Water has the ability to form clusters of various sizes which are reflective of microstructural
organizations of a fluid medium that undergoes marked dynamic alterations (Pan et al., 2004).
111
These water clusters then, even in large bodies of water, would possess unique electrical
properties reflective of the geometric organization of said cluster. The aggregate perspective
would support the electrodynamic fingerprint of a given material proposed by Burr.
6.1 Basic Properties of Water
If altering the structure of a given medium can change its electrodynamic properties so too
should altering the nature of water. Zheng and colleagues (2006) examined the exclusion zone of
water corresponding to the alignment of molecules along a boundary, whose results suggest that
the extent of the exclusion zone extended beyond 200 μm to 1 mm depending on the material
which served as an interface. Most notably, non-zero potentials could be detected between 120
and (-) 160 mV and which were altered by the addition of various chloride salts. The addition of
any salt to a solvent changes the solvation state (the degree of interaction of water molecules in
order to reduce the net charge distribution in the medium) of the molecules resulting in the
ultimate change in structure. Again, the causal change in structure is associated with a change in
the electrical potential of the substance lends credit to Burr's hypothesis.
Further alterations in the physical properties of water reflecting changes in its electrical
characteristics are presented by Teschke et al. (2001). Here, measures of the relative permittivity
of bulk and interfacial water were conducted and suggest that the permittivity of bulk water
decreased from 79 to 3.8 at the level of the interface (within 10 nm of the surface) between water
and a mica surface. Relative permittivity is a measure of the potential for a medium to generate
electric field flux. The greater the relative measure of permittivity, the greater the resistance for
the formation of an electric field. Conversely, a low relative permittivity increases the net
potential to generate an electrical field of flux within a medium.
112
Although not directly related to the electrical properties of water, changes in viscosity at an
interface can infer structural arrangements and electrodynamic changes. Goertz et al. (2009)
measured the viscosity of nanometer thick water films corresponding to interfacial water with
effective alterations in viscosity approaching 106 times greater than bulk water. Alterations of the
interacting media to a non-polar surface resulted in the absence of the enhancement of viscosity.
Viscosity (kg∙m-1∙s-1) is related to electric field strength (kg∙m∙A-1∙s-3) by way of magnetic
diffusivity (m2∙s-1) and the inverse of charge (As-1). If both the number of charges and the
magnetic diffusivity remain constant, any change in the magnitude of viscosity would result in a
proportional change in the electrical field strength of water. Consider the interface between water
and the membrane (a polar molecule) which is undergoing rotation, flipping and precession. The
resultant effect on the generated electric field would also respond to the dynamic nature of the
membrane and in turn become itself dynamic or time-varying. This hypothesis is merely
conjecture however all data supporting changes in the physical properties of water are conducted
in static conditions. Recall that even the structure of water itself is dynamic and consists of the
movement of charge amongst adjacent molecules.
The physical characteristics of hydronium dynamics were investigated by Lobaugh and
Voth (1996) who determined the activation energy of the proton transfer to H3O+ is
approximately 0.51 kcal/mol or 0.9 kbT and is based on the geometric assemblage of the solvent.
A charge in the concentration of available H3O+ invariably changes the dynamics of the system
and should be reflected in the structural organization of the solvent. The latter structural re-
organization in the presence of excess protons was investigated by Lobaugh and Voth (1996)
which revealed a smaller O-O separation which approximates 2.5 Å and allows for the flipping
of a proton between the pairs in a Grötthus-type mechanism. These inter-molecular O-O
113
distances corroborate the findings of Tuckerman et al. (1995) who also demonstrated that the
temporal duration of the hydration shell associated with the hydronium ion complex remains
intact (structured) for a duration of 2-3 picoseconds.
In order to maintain an equilibrium state hydronium ions must undergo a neutralization
reaction with its diametric opposition, the hydroxide ion whose time constants should also reflect
the same temporal duration as the generation of the hydronium ion itself. Mathematical
verification was performed by Hassanali et al. (2011) on the recombination of H3O+ and OH-
suggesting that the neutralization reaction involved a collective compression of oxygen along the
water wire corresponding to a time of 0.5 picoseconds and involved the simultaneous movement
of 3 protons. The proposed movement along the water wire, although occurring over a relatively
short duration, should express a relatively short distance of action. Hassanali et al. (2011)
determined that within a distance of 6 Å, the hydrogen wires coordinating the movement of
protons enhances the diffusion constant whilst outside this limit, diffusion constants are best
modeled according to bulk water parameters.
If water demonstrates the properties of changing electric fields then it should also be
affected by magnetic fields and thus can be modeled by Maxwell's relationships. Yamashita et
al. (2003) determined that the application of 60 Hz AC magnetic fields (~100 mG) and DC
magnetic fields (~1000 G) effected slow to large fluctuations in pH deviations and deviations on
oxidation reduction potentials of 60 mV. Gang et al. (2012) exposed samples of spring water to a
0.16 T horseshoe magnetic and determined an increase in viscosity that was maximized after 5.9
h, 7.4 h and 8.6 h corresponding to 25, 50 and 100 mL samples, respectively. Water exposed to
high intensity (14 T) magnetic fields intensities demonstrated a measurable change in peak
wavelengths by 1 - 3 nm (Iwaska and Ueno, 1998). Application of 1 μT intensity,
114
physiologically patterned electromagnetic fields which to water in the dark for 18 d
demonstrated a reliable 10 nm shift in peak fluorescence of water (Murugan et al., 2015) and
reliably demonstrates the ability of water to respond weak intensity electromagnetic fields.
Here we have also demonstrated that isolated samples of water exposed to rotating
electromagnetic fields (and temperature variations) reliably show a reduction in the net
magnitude of pH shifts over time. Furthermore, spatially isolated samples (1 m distances)
exposed to the appropriate configurations of rotating bulk angular velocity fields demonstrate
transient epochs of excess correlation without the addition of an exogenous stimuli. If water, or
at least the microstructural organization of water, denotes the conduit between the manifestation
of complex biological organizations and the necessary energetic equivalents driving the
processes responsible for the emergence of said organisms then the properties of water should
demonstrate a convergence upon the physical parameters of the entire set of observable variables
(the Universe) and the fundamental units which comprise the entire set (Planck scale). The
synthesis of dynamism and materialism should be inherently linked to a fundamental
organization of the Universe which reflects fundamental physical properties of the whole.
6.2 Casimir and Electromagnetic Interactions
When two parallel, non-conducting plates are brought into proximity with each other such
that the separation between the plates is significantly less than the surface area of the plates, a
negative force is generated between the plates (Plunien et al., 1986). The resultant force that is
generated has been coined the Casimir Force and reflects the possibility of transforming virtual
particles into real particles in the presence of a magnetic field. The force generated through a
Casimir-type effect is modeled by Equation (6.1):
115
Equation 6.1 :
where ħ is reduced Planck's constant (1.05∙10-34 kg∙m2∙s-1), a is the separation between the plates (meters) and A is
the surface of the plates (m2).
If we set the resultant Casimir force in the order of bond strengths (10-12 kg∙m∙s-2) and if the
distance between the parallel plates is set to the distance between adjacent oxygen atoms in a
solution containing excess protons (2.5 Å or 2.5∙10-10m), the surface area required to generate
forces in the order of bonds would be 3.62∙10-14m2. The resultant surface area is in the order of
the annulus surrounding a 1 μm axon. Taking the square root of the calculated surface area
results in a linear equivalent distance of 190 nm, which is well within the distances occupied by
the exclusion zone of water. Furthermore, if one treats the resultant linear equivalent of 190 nm
as a wavelength of light, energy can be calculated using Equation (6.2):
Equation 6.2: E =h
where E is the energy in Joules (kg∙m2∙s-2), h is Planck's constant (6.626∙10-34 kg∙m2∙s-1), c is the speed of light
(3.0∙108 m∙s-1) and λ is the wavelength of light in meters.
The resultant energy would be in the order of 1.05∙10-18 kg∙m2∙s-2. Furthermore, the temporal
equivalent of this wavelength can be approached using Equation (6.3)
Equation 6.3: t = d∙v-1
where t is the time (seconds), d is the distance (meters) and v is the velocity (m∙s-1).
Assuming, again, that the distance displaced is a wavelength of light in the order of 173 nm, the
relative speed of travel would the speed of light (3.0∙108m∙s-1) resulting in a temporal component
in the order of 5.76∙10-16 sec. The calculated temporal duration of 6.33∙10-16 sec is within the
limits of error for the duration of rotation of a Bohr electron and is complimented by the duration
of time it takes light, at c, to traverse the thickness of a cell membrane. Finally, given that the
116
spatial extent of a molecule of oxygen is approximately 3.0∙10-10 m, 6.3∙102 molecules would be
required to accommodate a distance of 190 nm. This latter organization is reflective of 10-20
clusters of water (40-50 molecule aggregates at varying temperatures).
Furthering the idea that water is the necessary conduit for the interaction between the
generation of structure (life) and the underlying processes that pre-determine the ultimate
geometric organization of the organism (the Blueprint), values reflective of the inherent electric
field generated by a single molecule should converge upon Universal constraints. Provided that
the energy of a water molecule is contingent upon the generation of the hydronium complex, the
energy associated with this process is ~ 2.0∙10-20 Joules (Persinger, 2010; Persinger, 2014). The
electric field strength can be dimensionally approached as the quotient of energy and the
electrical dipole moment of water (Equation 6.4).
Equation 6.4: Ε =
where E is the electric field (kg∙m∙A-1∙s-3), J is the energy in Joules (kg∙m2∙s-2) and p is the electric dipole moment
(Asm).
Given that the electric dipole moment of water is 1.85 Debye (6.17∙10-30 Asm) and the energy
associated with a water molecule is 2.0∙10-20 J, the solution produces an electric field strength in
the order of 3.2∙109 kg∙m∙A-1∙s-3. To relate the intensity of the electric field to the intensity of a
magnetic field (B) measured in Tesla (kg∙A-1s-2) we would simply have to divide by a speed. If
we wish to isolate a speed necessary to equate the inherent electric fields strength of water to the
intergalactic magnetic field we can use Equation 6.5:
Equation 6.5: v = E/B
where E is the electric field strength, B is the intensity of the magnetic field and v is velocity (m∙s-1).
117
Thus for the lower limit of the intensity of the intergalactic magnetic field (10-15T), and the
electric field strength of 3.2∙109 kg∙m∙A-1∙s-3, results in a speed in the order of ~1023 m∙s-1 and
reflects the theoretical limit of the entanglement velocity (Persinger and Koren, 2013).
The magnetic field strength squared can be dimensionally approached by the relationship
(Equation 6.6)
Equation 6.6: B2 =
where B is the intensity of the magnetic field, σ is the density of the material (kg∙m-3) and ε is the relative
permittivity of the material (A2s4∙kg-1m-3).
For water the density of the material is 103 kg∙m-3 and the relative permittivity along the
interfacial surface is 3.363∙10-11 A2s4∙kg-1m-3, the squared magnetic field strength would be in the
order of 2.97∙1013 T2. However, the energy stored within the magnetic field can be described by
the relationship (Equation 6.7):
Equation 6.7: E = ∙ V
where E is the energy of the magnetic field, B is the intensity of the magnetic field, µ is the permeability of free
space (1.256∙10-6 kgm∙A-2s-2) and V is the volume (m3).
Assuming that the resultant magnetic field strength squared of 2.97∙1013 T2, had an energy
equivalent of 2.0∙10-20 J, the resultant volume that is required to equate the two would be in the
order of 1.69∙10-39m3.
6.3 Gravitational Considerations
Similarly, the gravitational potential energy can be calculated using Equation (6.8):
Equation 6.8: Eg = G∙
118
where Eg is the gravitational potential energy is Joules, G is the gravitational constant (6.67∙10-11 m3∙kg-1s-2), m is
mass (kg) and r is the separation between the masses.
Setting Equation 6.7 equal to 6.8 and isolating for a given separation between water molecules in
order to accommodate the equivalence is represented in Equation 6.9:
Equation 6.9: r =
Thus the separation between water molecules having mass 3.0∙10-26 kg, with approximately
6.3∙102 molecules that occupy the exclusion zone, the distance must be 1.15∙10-36 m or within the
error of a Planck's length (1.616∙10-35 m). Consider the possibility that two water molecules
approach the limit of separation in the order of Planck's length. The resultant energy associated
with two non-conducting surfaces can be approached using a Casimir phenomenon and can be
calculated using the equation (6.10).
Equation 6.10: Ec = A
where a is the separation between the plates, A is the surface area of the plates, c is the speed of light and ħ is
reduced Planck's constant.
Continuing with the hypothesis that two water molecules approach the limit of separation
of a Planck's length, then the surface of the plates would be in the order of the molecular
diameter of water which is 10-12m2 and the separation between the molecules would be 1.15∙10-36
m. The calculated resultant Casimir energy would be in the order of 1069 J, reflecting the total
energy stored within the Universe (Persinger, 2013; Persinger, 2014; Persinger 2015). Further
support for the convergence of water as a mediator for the transition from Universal operational
parameters and the potential for the generation of the organism would be contingent upon
homogeneity between the photon and the graviton. One can calculate the change in magnetic
moment of an object operating in a magnetic field with Equation 6.11.
119
Equation 6.11: Δm = B
where Δm is the change in magnetic moment (kg∙A-1s-2), e is the charge of the electron (1.6∙10-19As), r is the distance
between water molecules (2.5∙10-10m) me is the mass of the electron (9.11∙10-31 kg) and B is the applied magnetic
field.
Dimensionally analyzing Equation 6.11 ultimately results in a magnetic moment (Am2) which
can be further digested as the aggregate of a Joule divided by a Tesla. In this instance in order to
accommodate an energy we would simply have to modify Equation 6.11 producing Equation
6.12.
Equation 6.12: E = B2
where E is the energy obtained in Joules and all other parameters are consistent with Equation 6.11.
When one simply inputs all variables except for a value for B, an approximate value of 1.74∙10-26
A2s2m2∙kg-1. Setting an external field to a measure of 1 T would demonstrate a change in
magnetic moment of water in the order of 1.74∙10-26Am2 or effectively the difference in magnetic
moment between the spin magnetic moment of the electron and the orbital magnetic moment of
the electron. Now consider an applied magnetic field in the order of 10-13 T, or the intensity of
the intergalactic magnetic field. In this instance the net energy that would result from Equation
6.12 would be in the order of 10-52 Joules. This may be an inherently small amount of energy
however if we consider the mass equivalent of this energy at rest (when c is set to 1) then the
mass, as derived by the Einstein-Eddington equation (6.13) would be in the order of 10-52 kg or
the upper limit of the rest mass of the photon (Tu et al., 2005; Luo et al., 2033).
Equation 6.13: E = mc2
Furthermore, consider the application of weak intensity magnetic fields in the order of 3.0 nT
(values that were utilized in our experiments), the resultant energy calculated from Equation 6.12
120
would be in the order of 10-44 Joules. Again the mass equivalent of this energy, as calculated
using the Einstein-Eddington Equation (6.13) would isolate a mass in the order of 10-60 to 10-61
kg. This range of mass is well within the order of magnitude of the graviton (Gershtein et al.,
1997; Novello and Neves, 2002; Goldhaber and Nieto, 1974; Goldhaber and Nieto, 2010).
Another parameter that can be extracted from the approach offered here is a temporal component
of water operating in a nanoTesla strength field. Given that the energy obtained from the change
in magnetic moment of water in a 3.0 nT field is in the order of 10-43 to 10-44 J a frequency can be
calculated using Equation 6.14.
Equation 6.14: f =
where f is the frequency (s-1), E is the energy in Joules (kgm2∙s-2) and h is Planck's constant (6.626∙10-34 kgm2∙s-1)
Given a mean value of 5.0∙10-43 Joules for water in a 3.0 nT field, the resultant frequency would
be 7.55∙10-10 s-1. The inverse of a frequency would be a time, thus the temporal equivalent of
7.55∙10-10 s-1 would be 1.325∙109 sec which is within the order of the approximate lifetime of a
human being. Could it be that the inherent construction of our being is strictly limited in its
duration not by the complex activity of biochemical agents or cellular constituents, but by the
innate nature of water and its interaction with weak intensity magnetic fields?
6.4 The Exclusion Zone
Stepping aside from Universal hybridization of water into other physical parameters we
examine the physical nature of the exclusion zone. It is suggested that a net increase in the
viscosity of the exclusion zone occurs such that the increase is within ~106 times larger than the
normal viscosity of water. Here we endeavour to determine the effects that could mediate such
an interaction using force as an intermediate. The general linear expansion of the exclusion zone
suggests a relative mean occupancy in the order of 100 μm. Given that that the molecular
121
diameter of a water molecule is 3.0-10 m the total amount of water molecules that would occupy
the linear equivalent of 100 μm would be approximately 3.0∙105 molecules. Given that the bond
strength of water is in the order of 10-12 N (kgm∙s-2) the net force is calculated as the product of
the force of a single bond and the total available molecules and would approach 10-6 to 10-7 N.
Given the upper limit of force is 10-6 N we can approach the viscosity as the quotient of the force
by a diffusion constant (Equation 6.15).
Equation 6.15: η=
where F is force (kgm∙s-2), υ is the diffusion constant (m2∙s-1) and η is viscosity (kg∙m-1s-1).
Given that the force is in the order of 10-6 and the diffusion mobility constant of water to be in
the order of 3.6∙10-7 m2∙s-1, the viscosity would be in the order of 101. Comparatively, given the
lower limit of force across the exclusion zone to be in the order of 10-7 N, and substituting the
diffusion constant for that of water (8.65∙10-9m2∙s-1) the magnitude of the viscosity would
approach 102 kg∙m-1s-1. Given that the viscosity of bulk water is 8.94∙10-4kg∙m-1s-1, these
calculated values would be 105 to 106 times greater and reflects what Goertz et al. (2009) found
in their experiments.
Extending the previous section we can find an energy equivalent of 10-7 N as energy is the
product of force and distance. Provided that the force of the linear equivalent of the exclusion
zone extends the length of the exclusion zone (10-4m) the resultant energy would be in the order
of 10-11 Joules. One can isolate a magnetic field strength associated with the energy of the
magnetic field using the equation (6.16)
Equation 6.16: B =
122
where B is the intensity of the magnetic field (kg∙A-1s-2), E is the energy in Joules, μ is the permeability of free space
and V is the volume.
If we assume that the volume of water is consistent with the volumes used in our experiments
(25 mL) then the strength of the magnetic field would be in the order of 10-6 Tesla. This value is
equivalent to the magnetic field generated by the firing of all the neurons in the brain. Similarly,
if one substitutes the volume in Equation 6.16 with the approximate volume of the human brain
(10-3m3) the resultant magnetic field strength would be in the order of 10-7 T, or the equivalent of
109 neurons firing or the equivalent of high intensity geomagnetic perturbations (Persinger,
1983).
6.5 Photon-Graviton Entanglement in Water
Given that the background radiant flux density of photons is in the order of 2.0∙10-12W∙m-
2 (kg∙s-3) the convergence upon the level of water should be evident. Here if we assume that the
flux density is related to the magnetic density an equivalence can be equated such that (equation
6.17):
Equation 6.17: B = Φ∙I∙f
where B is the magnetic field, I is the current (A) f is frequency, and Φ is radiant flux density (kg∙s-3)
Assuming that the effective intensity of an incident magnetic field is ~10-9 T, to homogenize the
structure of water with background parameters of the Universe, then the aggregate of current and
frequency would represent a current per unit time (A∙s-1). The necessary value to equate a
magnetic field strength of 1 nT and background radiant flux density of 10-12 kg∙s-3 would be in
the order of 2-3∙10-3 A∙s-1.
Consider that the Universe is composed of 1.58∙1079 particles, Eddington's number, and
that each particle has a charge equivalent of 1.6∙10-19 As. The resultant net Universal charge
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would be 2.53∙1060 As (Persinger, 2015b). Here if we take the quotient of the net Universal
charge and a current of 2-3∙10-3 A∙s-1 it results in a frequency squared of 1.14∙10-63s-2, or
frequency equivalent of 3.37∙10-32 s-1. An energy equivalent of this latter frequency can be
calculated by multiplying by Planck's constant (6.626∙10-34 kgm2∙s-1) and would be 2.24∙10-65
Joules. The rest mass equivalent (when c is set to 1) of an energy of 2.17∙10-65 Joules would be
2.24∙10-65 kg, or the upper limit of the rest mass of the graviton (Gershtein et al., 1997; Novello
and Neves, 2002; Goldhaber and Nieto, 1974; Goldhaber and Nieto, 2010). Furthermore, if we
take the product of the mass of 2.24∙10-65 kg and the square of the entanglement velocity
(1023m∙s-1) the resultant energy would be in the order of 10-18 to 10-19 Joules converging on the
approximate energy of 190 nm light which was determined to be inherently linked to water.
If Mach's principles of the eminence of the Universe (Mach, 1887) hold true then the
convergence upon 2-3∙10-3 A∙s-1 should also be present in the properties of water. Dimensionally,
the inherent current per unit time can be calculated by the product of conductivity, electric field
strength and a diffusion parameter and is represented in equation 6.18.
Equation 6.18: = σ ∙ E ∙ υ
where I/t is current per unit time (A∙s-1), σ is the conductivity (A2s3∙kg-1m-3), E is the electric field (kgm∙A-1s-3) and υ
is diffusion (m2∙s-1).
Provided that the current is in the order of 2-3∙10-3 A∙s-1, the average conductivity of water is
1.75∙10-4 A2s3∙kg-1m-3, and the inherent electric field of a given water molecule was calculated to
be 3.2∙109 kgm∙A-1s-3, the resultant diffusion constant would be in 4.46∙10-9m2∙s-1 or within the
error of the diffusion constant of water (Bett and Cappi, 1965). This suggests that the
convergence between light, magnetic fields and the graviton is reflected or mediated by the
simple diffusion of water over a given temporal span. The persistence of the homogeneity of
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physical parameters converging at the level of water consistently reflects the possibility that
water may act as a conduit for necessary for the interaction of the energetic equivalent of the
Blueprint for Immortality to interact in our physical world.
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Chapter 7
7 Conclusion
We have systematically examined three different geometric organizations (water, bacteria
and plants) in order to identify a potential homogenous substrate that acts as a binding agent to
conform to Burr's Blueprint hypothesis. Although we have not definitively identified a universal
agent capable of binding distinct forms of information/energy resulting in complex aggregates,
we have identified a convergent level of discourse that has allowed for the generation of distinct
forms of life. It is suggested from both theoretical and experimental perspectives that water may
be the essential conduit necessary to extract the relevant energy in order to structure, at least, the
basic elements required for the generation of life.
We have successfully identified that whole plants (Carex stricta) demonstrate conspicuous
alterations in electrodynamic profiles when they undergo changes in their ultrstructural
organization (i.e. senescence). Furthermore, significant changes in electrical potential measures
of these plants were modified by peak intensity light stimulation, reflecting a threshold that
converges at the level of the cell (described as being a quantum-cell equivalent) driving the
potential transition into and out of senescence. We proposed the complementarity between
electric and magnetic fields whereby the alteration in one can ultimately affect the other.
In this particular experiment consider the essence of the incident light radiation which was
distinctly capable of altering the electrodynamic profiles of plants. Light is a form of
electromagnetic radiation and thus contains both electric and magnetic properties within its wave
function. Theoretically, the incident electromagnetic radiation can interact with the material and
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alter its electric field parameters alongside the addition of a discrete amount of energy which is
essential for the appropriate operation of the organism. Outside interaction with material, that is
the interaction between magnetic and electric fields in a vacuum, one assumes the direct
implication of the interference between both the electric and the magnetic fields to occur
simultaneously. However, if one component of the electromagnetic dichotomy must interact with
a material intermediary, the process of alteration in one of the measured components would
require a temporal latency. Effectively, the interaction of one component of the electromagnetic
spectrum interacts with the material through the exchange of energy. This energy is then utilized
by the material, in this case living plant tissue, in order to alter some component, or geometric
assemblage, resulting in a change in the temporal electrodynamic properties of the organism. In
our experiment a measurable change in the electric field of the organism was observed, however
it was not instantaneous which suggests that the organism required time to respond to the
stimulus and supports our presented hypothesis of a structural intermediary.
From our first experiment it was imperative to identify a relatively homogenous substrate for
the interaction of electromagnetic fields that pervades multiple levels of discourse. Thus we
examined the possibility of two distinct species (plant and bacteria) separated in space and
exposed to rotating electromagnetic fields to share a process. When a germinating seedling is
presented with a given stimulus and a sample of bacteria some distance away and not presented
with a stimulus, and both these of samples are exposed to the same configurations of rotating
electromagnetic fields, the response of the plant material can be represented in the bacterial
sample. If both bacteria and plants demonstrate the same alteration in their electrodynamic
profiles (that variable which is being measured) when subjected to the appropriate elicitation of
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excess-correlation then it is not the structural dissimilarities which bind them but their
similarities which allows them to reciprocally interact.
The results presented in our third chapter demonstrate the potential for spatially separated
and phylogenically separated species exhibit the possibility to become excessively correlated
with each other. In this sense we postulate that there exists candidate(s) for the potential to
interact with the underlying material organization which is representative of the distinct species.
The results of the this experimental design demonstrated two distinct geometries, consisting of
discrete 1 and 3 msec point durations, which were successful at maximizing the convergence of
temporally correlated activity in both species. We identified that the appropriate application of
forward and reverse presentation parameters were successful at generating excess correlation
between species and formed what was described as a gradation of appropriate application
geometries necessary to elicit the excess correlation effect. The convergence upon the duration of
the points (stimuli) exhibiting the potential to effectively elicit excess correlation overlaps with
quantitative solutions reflecting the nature of the proton and electron, respectively. The idea of a
gradation associated with the effective application of excess correlation eliciting fields and their
reversed configurations leads to the postulate that the material of interaction may, most likely, be
a common source between the species. We also postulate that the gradation of effectiveness is
contingent upon the differences identified between the species and may reflect a conserved
ultrastructural arrangement.
In the previous section we suggested and presented supporting evidence that there exists a
common variable associated with the excess correlation between different species allowing for
the interaction to persist. In light of this idea we have eliminated all other variables, i.e. similar
and different structures that are associated with the organization of the respective bacterial and
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plant organisms, and have strictly examined the nature of water as being the potential variable
which binds the interaction between incident electromagnetic radiation and the resultant
alteration in the electric field properties of the system. Chapter 4 devotes its essence to the
application of 3 and 1 msec accelerating and decelerating field presentations and the possible
effects on water. Two geometries were identified as capable of eliciting passive excess
correlation in spatially separated sources of water which consisted of 3 msec point durations
serially applied in a decelerating-accelerating pattern and 1 msec points in an accelerating-
decelerating pattern were effective at eliciting the excess correlation effect.
The resultant interaction between water and electromagnetic fields provides us with two
further insights into the nature of the Blueprint for Immortality. The first insight suggests that
excess correlation does not require a driving force to initiate its processes provided that the
material itself demonstrates the capacity of displaying transient processes. The second insight
derived from this experiment implicates water as the most likely candidate driving the
homogenization permitting the interaction between distinct entities. There exists a marked
overlap in the applied electromagnetic field configurations which reflect the possibility of
eliciting excess correlation in distinct species and those field configurations which effectively
demonstrated correlated changes in the electrical potential of isolated water samples.
Seemingly, it is water that may be the most probable candidate for the maximized alterations
associated with the proposed electromagnetic-material interaction. The electromagnetic-material
interactions sub-serves the primary method by which energetic packets of information, consistent
with the genesis of life, bind to structure. The biding of information to structure elicits the
appropriate geometric alterations directing the aggregation of units into the assemblages of the
basic units which contribute to the formation of life. With respect to water, the latency by which
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a hydronium complex requires to form and the duration of its existence are equivalent. In this
particular context, energy can interact with material or it can direct a process and the result of
that process can interact with a material. Any alteration that occurs upon the transition from
energetic equivalents to structural equivalents will be represented in the final product. In the case
of a finite being such as a cell or a full scale organism, the application of discrete packets of
information are held in place via the prolonged structural organization of the system. The biding
to matter is essential to the formation of an organism as the resultant structural analogues do not
change readily over time. However in the case of water, the exchange of information and its
integration into structure occurs so quickly that any detrimental alteration that could possibly
arise from its incorporation into the aggregate is dismissed. This dismissal is due to the latency
of the formation of the intermediary complex into which information can be stored. It is here at
the interface between the formation and persistence of structure with information which can be
incorporated to direct the essential genesis of the complex organism. Ultimately the structure
integration of all units is reflected in the electrodynamic profile of said organism. As we have
identified, the transition between formation and structural retention in water proceeds on a time
scale that would not allow for the genesis of ordered states let alone highly specialized cell types.
If water is the generative source for our existence there must have previously existed
systematic alterations in exogenous variables which in turn invariably reduced the latency of the
degradation of the formation of the hydronium complex allowing for the proliferation of more
complex structures. The notion of a systematic alteration in the dynamics of water leading to the
prolonged organization of aqueous domains is summarized in the final experiment of this
document. The results from this experiment suggest that the combination of near 0⁰C
temperatures and accelerating bulk angular velocity field application with 1 msec point durations
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maximizes the reduction in absolute magnitudinal shifts of pH in response to injection of weak
acids and increases the latency of said alterations. The delay and reduction of the magnitude of
the pH shift upon the addition of acetic acid suggests an extended organized domain of water
which, in this instance, demonstrated the ability to exclude protons within a given expanse of
water. These results suggest that temperature reductions and the appropriate magnetic field
configurations can structure a dynamic system such that its physiological properties are altered
and that the system essentially becomes a static organization. In this instance, we have provided
a possible mechanism which would allow for the prolongation of structured domains of water.
These domains produce an impermeable region and may reflect the necessary contingencies
required to initiate the formation of prototypical cellular constructs. Taking all the presented
information together, we suggest that water acts as the conduit for the formation of complex
organizations of geometries resulting in the emergence of proto-typical parameters necessary for
the genesis of life-forms whose directed organization is mediated by the fundamental interaction
between matter and electromagnetic fields.
